Tag: nevin manimala

Vet Rec. 2026 Mar 13. doi: 10.1002/vetr.70499. Online ahead of print.

ABSTRACT

BACKGROUND: Data evaluating paracetamol combined with NSAIDs in dogs are scarce. Results of clinical studies in dogs investigating intravenous paracetamol vary.

METHODS: Dogs were randomised to either receive 10 mg/kg paracetamol intravenously after induction of anaesthesia and every 8 hours during hospitalisation (test) or not (control). In both groups, meloxicam or robenacoxib was administered at the licensed dose, interval and route. Intraoperative nociception or postoperative pain, defined by a Glasgow composite measure pain scale-short form score greater than 4 of 20, was treated with 0.1 mg/kg methadone intravenously as needed.

RESULTS: Data from 14 dogs that received paracetamol and 13 dogs that did not were analysed. There were no statistically significant differences in clinical or demographic data between the two groups. Median (range) rescue methadone requirements were 0.0 (0.0‒0.2) mg/kg and 0.1 (0.0‒0.3) mg/kg for the test and control groups, respectively (p = 0.17), with no difference intraoperatively (p = 0.72) or postoperatively (p = 0.24). Four test and seven control dogs required rescue analgesia perioperatively (p = 0.17).

LIMITATIONS: Low analgesia requirements in both groups may have resulted in a type two statistical error.

CONCLUSION: When used as part of a multimodal analgesic protocol, paracetamol did not provide significant opioid-sparing effects in the perioperative period.

PMID:41826244 | DOI:10.1002/vetr.70499

Vet Rec. 2026 Mar 13. doi: 10.1002/vetr.70495. Online ahead of print.

ABSTRACT

BACKGROUND: Non-ambulatory tetraparesis or tetraplegia in cats may constitute a diagnostic challenge for general practitioners. Therefore, this study aimed to evaluate if clinical variables from signalment, history, clinical examination and basic ancillary tests are associated with underlying diagnoses in cats with non-ambulatory tetraparesis or tetraplegia.

METHODS: This was a retrospective single-centre study of cases presented between 2010 and 2023. Information on disease onset, progression, neurological and physical examination findings and ancillary tests was analysed across all diagnoses. Diagnostic categories comprising five or more cases were carried forward to univariate and/or multivariable analyses.

RESULTS: Eighty-one cats were included, with 82.7% of cases represented by six conditions: polyneuropathy (PN; n = 26), ischaemic myelopathy (IM; n = 15), spinal cord neoplasia (n = 8), feline infectious peritonitis (n = 7), intracranial neoplasia (n = 6) and spinal cord contusion (n = 5). On multivariable analysis, an age of below 3 years, progressive presentation, normal mentation, reduced spinal reflexes in the thoracic limbs and normal blood tests were statistically associated with PN. Age between 6 and 9 years or older than 9 years and peracute onset of clinical signs were associated with IM.

LIMITATIONS: This was a retrospective study with limited multivariable analysis in certain diagnostic categories. Furthermore, the study included only referral cases, which may not represent the animal population seen by general practitioners.

CONCLUSIONS: PN and IM were the most common causes for non-ambulatory tetraparesis or tetraplegia in this population of cats. Attention to the neurological examination and easy to identify clinical features can be used to determine the most likely differential diagnoses and assist general practitioners in the formulation of a diagnostic plan.

PMID:41826233 | DOI:10.1002/vetr.70495

Semin Nucl Med. 2026 Mar 12:S0001-2998(26)00044-9. doi: 10.1053/j.semnuclmed.2026.02.002. Online ahead of print.

ABSTRACT

Nuclear medicine infection imaging has traditionally relied on semantic visual interpretation supported by simple semi-quantitative indices. While effective, this paradigm is limited by observer dependence, restricted sensitivity to subtle or diffuse disease, and difficulty in standardising interpretation across centres. Advances in quantitative imaging, radiomics and artificial intelligence (AI) are reshaping this landscape. These complementary domains, collectively conceptualised as computomics, extend infection imaging from qualitative pattern recognition toward objective, reproducible and data-driven characterisation of disease. Quantitative imaging converts tracer distribution into measurable biological metrics, ranging from simple region-of-interest count ratios to standardised uptake values and kinetic parameters. Radiomics builds on this foundation by extracting high-dimensional features describing intensity, shape, texture and spatial heterogeneity, revealing image information not appreciable to the human eye. AI, through machine learning and deep learning approaches, integrates quantitative and radiomic data with clinical variables to automate segmentation, enhance reconstruction, support classification, and enable predictive modelling. Together, these tools offer potential to improve differentiation of infection from sterile inflammation, quantify disease burden, monitor therapy response, and standardise interpretation in complex scenarios. Quantitative accuracy and radiomic stability remain highly dependent on acquisition, reconstruction and processing parameters. AI-driven image enhancement and denoising may improve visual appearance while altering voxel statistics, with downstream effects on quantitative metrics and texture features. Variability in feature definitions, segmentation methods and analysis pipelines further limits reproducibility. Consequently, harmonisation, standardisation, transparent validation and physics-informed AI models are essential.

PMID:41826111 | DOI:10.1053/j.semnuclmed.2026.02.002

J Nutr. 2026 Mar 12:101398. doi: 10.1016/j.tjnut.2026.101398. Online ahead of print.

ABSTRACT

BACKGROUND: Data from randomized controlled trials of vitamin D₃ supplementation in modifying the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are sparse.

OBJECTIVES: We evaluated the effect of vitamin D₃ supplementation on healthcare utilization and other clinical outcomes among adults with coronavirus disease 2019 (COVID-19) and their close contacts.

METHODS: We conducted a parallel 2-group randomized controlled double-blinded trial targeting free-living adults in the United States and Mongolia. Index participants with newly diagnosed COVID-19 were cluster-randomized with up to one of their cohabiting contacts either to an oral vitamin D₃ loading dose of 9600 IU/d for 2 d followed by 3200 IU/d for 4 wk or to placebo. Participants completed weekly questionnaires on healthcare utilization, disease severity, and long COVID (index participants) or new SARS-CoV-2 infection (household contacts). The primary outcome was ≥1 healthcare visits (including hospitalization) or death within 4 wk among the index participants.

RESULTS: Index participants (n = 1747) were a median of 38.0 y old (IQR: 31.1-47.0), 65.6% female/other sex, 4.2% Black non-Hispanic, 4.8% Hispanic/Latinx, 43.2% Asian, 44.3% non-Hispanic White, and 44.9% vitamin D deficient or insufficient (25-hydroxyvitamin D₃ <20 ng/mL). Baseline characteristics for the household contacts (n = 277) were similar. The 4-wk cumulative incidence of healthcare utilization in index participants did not significantly differ between the vitamin D₃ (n = 863) and placebo (n = 884) groups [cumulative incidences, 0.28 compared with 0.29; odds ratio (OR), 0.97; 95% confidence interval (CI): 0.75, 1.24]. Similar nonsignificant results were observed for the prespecified secondary treatment and prevention outcomes, though per-protocol analyses showed a nonsignificant trend toward benefit of vitamin D₃ on the prevalence of long COVID at 8 wk (OR, 0.78; 95% CI: 0.59, 1.03). No safety concerns were identified.

CONCLUSIONS: Among adults with newly diagnosed SARS-CoV-2 infections, vitamin D₃ supplementation did not significantly change the 4-wk cumulative incidence of healthcare utilization or COVID-19-related outcomes compared with placebo. Promising results for long COVID warrant further study. This study was registered at clinicaltrials.gov as NCT04536298. First registered on 1 September, 2020.

PMID:41826107 | DOI:10.1016/j.tjnut.2026.101398

J Prosthet Dent. 2026 Mar 12:S0022-3913(26)00090-9. doi: 10.1016/j.prosdent.2026.02.009. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: Systematic reviews (SRs) are time-consuming and resource-intensive processes. Whether large language models (LLMs) can improve the process is unclear.

PURPOSE: The purpose of this study was to evaluate the accuracy and reliability of 4 LLMs (GPT-4, Gemini, Claude, and Elicit) in performing SR tasks (full-text screening, data extraction, and risk of bias assessment) at 3 different sequential periods of time (0, 15, and 30 days).

MATERIAL AND METHODS: A comprehensive systematic search was conducted across 5 databases in December 2024, with 59 articles evaluated for screening (2 used for pilot) and 31 for data extraction (2 used for pilot). A 3-pronged prompting strategy was used, including persona-based initialization, few-shot learning, and structured population, intervention, control, outcome (PICO) criteria. Performance was assessed through 3 repeated evaluations at 2-week intervals by measuring accuracy and reliability using standard metrics (accuracy, precision, F1-score, sensitivity, and specificity) against expert assessments, data extraction quality on a 0 to 5 scale, and risk of bias agreement via the Cohen kappa, with statistical analysis using Kruskal-Wallis and Dunn post-hoc tests (α=.05).

RESULTS: In full-text screening, Claude achieved the highest sensitivity at 97%, while Claude and Elicit both showed strong overall performance with 86% accuracy and 87% F1-scores. All models maintained sensitivity above 90%. For data extraction, GPT-4 consistently performed best with median scores of 5.0, while Claude and Gemini showed similar capabilities. Significant differences only appeared in labeling and modeling tasks during Week 1 (P=.04). Risk of bias assessment agreement with experts varied from 55% to 90% across different criteria.

CONCLUSIONS: LLMs show potential for SR efficiency (especially for data extraction) but require human oversight because of variable performance across models and tasks.

PMID:41826091 | DOI:10.1016/j.prosdent.2026.02.009

Orthop Surg. 2026 Mar 13. doi: 10.1111/os.70277. Online ahead of print.

ABSTRACT

BACKGROUND: In the multiple ligament injury of the knee joint, apart from the anterior cruciate ligament and the posterior cruciate ligament, the Posterolateral Corner and the Posteromedial Corner are two structures that are easily overlooked. If not properly identified and repaired in one stage, the knee joint may be unstable, even failure of cruciate ligament reconstruction. The purpose of this article was to evaluate the effect of knee joint recovery after PLC (Posterolateral Corner) with or without PMC (Posteromedial Corner) injury.

METHODS: From 2016 to 2020, we screened a total of 2564 patients, of which 292 patients met the inclusion and exclusion criteria. In the end, a total of 44 people completed the study. Follow-up was performed at 1, 3, 6, 9, and 12 months after surgery. We used pain visual analog scale (VAS) for pain, IKDC score, Lysholm score, Tegner score. Opti-knee (a portable motion analysis system) was used to evaluate the stability of the knee joint at 1 year. In our prospective cohort study, we used the unpaired Student’s t-test for statistical analysis.

RESULTS: The knee joint function of PLC group and PLC combined PMC group was better than that before operation at 3 or 6 months after operation. Except for IKDC at 9-month follow-up and Tegner score at 9-month and 12-month follow-up, there was no significant difference between the other groups.

CONCLUSIONS: PLC and PLC combined with PMC injury showed similar prognostic effects, although the PLC group was numerically superior to the other group. We recommend primary repair and reconstruction in patients with confirmed PLC and PMC injuries to achieve the best postoperative recovery.

PMID:41826073 | DOI:10.1111/os.70277

Appl Nurs Res. 2026 Apr;88:152061. doi: 10.1016/j.apnr.2026.152061. Epub 2026 Feb 10.

ABSTRACT

BACKGROUND: Information literacy and innovative behavior are key factors influencing evidence-based practice (EBP) competency in clinical nurses, yet the mechanisms underlying these associations remain unclear.

AIM: To examine the relationships among information literacy, innovative behavior, and EBP competency in clinical nurses, and to explore whether innovative behavior mediates the relationship between information literacy and EBP competency.

METHODS: A multicenter cross-sectional study was conducted from December 2023 to January 2024. Using convenience sampling, 1111 clinical nurses were recruited from three tertiary hospitals in Lishui City, Zhejiang Province, China. Data were collected using the Information Literacy Scale, Innovative Behavior Scale, and EBP Scale. Pearson correlation analysis assessed associations, and Mplus 8.3 tested the mediating effect.

RESULTS: A total of 1092 valid questionnaires were analyzed. Mean scores for information literacy, innovative behavior, and EBP competency were 76.86 ± 10.76, 33.70 ± 7.04, and 36.60 ± 5.29, respectively. EBP competency was positively correlated with both information literacy (r = 0.642, P < 0.01) and innovative behavior (r = 0.583, P < 0.01). Information literacy was also positively correlated with innovative behavior (r = 0.675, P < 0.01). Innovative behavior partially mediated the effect of information literacy on EBP competency (indirect effect = 0.159), accounting for 22.68% of the total effect.

CONCLUSION: Clinical nurses demonstrated above-average EBP competency. Strengthening information literacy and promoting innovative behavior may directly and indirectly enhance EBP competency.

PMID:41826038 | DOI:10.1016/j.apnr.2026.152061

Appl Nurs Res. 2026 Apr;88:152067. doi: 10.1016/j.apnr.2026.152067. Epub 2026 Feb 9.

ABSTRACT

BACKGROUND: Sleep quality is an important factor that can affect individuals’ work performance and productivity. This study was conducted to determine the relationship between sleep quality in nurses and their clinical decision-making, as well as the tendency to make medical errors.

METHODS: A descriptive and correlational survey design was used. A total of 366 nurses participated in the study. Data were collected using a Demographic Information Form for Nurses, the Pittsburgh Sleep Quality Index (PSQI), the Clinical Decision-Making in Nursing Scale (CDMNS), and the Medical Error Tendency Scale in Nursing (METSN).

RESULTS: The average total score of nurses on the PSQI was 7.2 ± 3.1, the average total score on the CDMNS was 97.7 ± 13.1, and the average total score on the METSN was 229.2 ± 18.7. The relationship between the total scores of the CDMNS and the METSN with the components of the PSQI was examined using path analysis. The path coefficients between the PSQI Subjective Sleep Quality and the total score for the CDMNS (β = 0.012; p < 0.001) and the total score for the METSN (β = 0.004; p = 0.045) were found to be statistically significant.

CONCLUSION: This study found a significant relationship between nurses’ sleep quality and their clinical decision-making and tendency to make medical errors. This study shows that the quality of sleep of nurses is a crucial factor influencing their clinical decision-making skills and their tendency to make medical errors.

PMID:41826037 | DOI:10.1016/j.apnr.2026.152067

Appl Nurs Res. 2026 Apr;88:152065. doi: 10.1016/j.apnr.2026.152065. Epub 2026 Feb 5.

ABSTRACT

BACKGROUND: Moral distress is increasingly recognized as a critical challenge in critical care settings, where nurses frequently encounter ethically complex situations that can undermine their moral integrity. With the growing adoption of artificial intelligence (AI) in clinical decision-making, there is a need to understand whether AI integration may help mitigate the ethical burden experienced by nurses.

AIM: This study aims to examine the relationship between moral distress and moral integrity and investigate the mediating role of AI integration in this association.

METHODS: A cross-sectional correlation design was employed among 250 ICU nurses recruited from three private hospitals. Data was collected using three structured questionnaires. Descriptive statistics, Pearson correlations, multiple regression, and structural equation modeling (SEM) were used to examine direct and indirect effects.

RESULTS: Moral distress was negatively associated with both AI integration (r = -0.42, p < 0.01) and moral integrity (r = -0.56, p < 0.01), while AI integration demonstrated a positive association with moral integrity (r = 0.48, p < 0.01). The regression model explained 48% of the variance in moral integrity. SEM results indicated that AI integration partially mediated the association between moral distress and moral integrity (indirect β = -0.16, p < 0.001).

CONCLUSION: Moral distress significantly undermines nurses’ moral integrity, yet AI integration provides a partial protective effect by enhancing ethical clarity and decision support. While AI cannot fully offset the impact of chronic moral strain, it represents a promising tool to strengthen ethical practice in critical care environments.

PMID:41826035 | DOI:10.1016/j.apnr.2026.152065

Appl Nurs Res. 2026 Apr;88:152062. doi: 10.1016/j.apnr.2026.152062. Epub 2026 Feb 5.

ABSTRACT

BACKGROUND: Lifelong learning, counseling self-efficacy, and professional self-doubt are key determinants of nursing excellence, particularly in high-stress environments such as Emergency Departments (EDs) and Critical Care Units (CCUs). Understanding how these factors interact can inform strategies to enhance professional development and patient-centered care among nurses.

OBJECTIVE: This study aimed to examine the relationships between lifelong learning tendencies, counseling self-efficacy, professional self-doubt, and perceptions of nursing excellence among emergency and critical care nurses in Turkey.

METHODS: A descriptive cross-sectional design was employed with 744 registered nurses from 14 hospitals (8 governmental, 6 private). Participants were recruited via convenience sampling, and data were collected through a structured online questionnaire. Instruments included the Lifelong Learning Tendencies Scale (LLLTS), Counseling Self-Estimate Inventory (COSE), Professional Self-Doubt Scale (PSD), Good Nurse Questionnaire, and Better Nursing Questionnaire. Descriptive statistics summarized participant demographics and scores, while Pearson correlation coefficients examined relationships among the study variables.

RESULTS: Nurses reported moderately high lifelong learning tendencies (mean = 4.12, SD = 1.65), with motivation and persistence as the strongest sub-dimensions. Counseling self-efficacy was generally high (mean = 4.75, SD = 0.77), particularly in micro-skills and cultural competence. Professional self-doubt was moderate (mean = 2.82, SD = 1.07), with higher uncertainty reported when nurses felt unable to influence patient outcomes. Positive correlations were found between lifelong learning and professional competency (r = 0.41, p = 0.003), sense of achievement (r = 0.36, p = 0.009), expertise in nursing (r = 0.42, p = 0.002), and counseling self-efficacy subdomains, while professional self-doubt correlated negatively (r = -0.29, p = 0.049).

CONCLUSIONS: Nurses demonstrating stronger professional competence, confidence in counseling skills, and a sense of accomplishment were more inclined toward continuous learning and professional development. These findings underscore the importance of fostering lifelong learning and counseling self-efficacy while addressing professional self-doubt to enhance nursing excellence in critical care and emergency settings.

PMID:41826033 | DOI:10.1016/j.apnr.2026.152062