EP-182 The predictive significance of neutrophil-to-lymphocyte ratio in cholecystitis: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Aims The aim of this review was to examine whether neutrophil-to-lymphocyte ratio (NLR) can predict the presence of cholecystitis and distinguish between simple and severe cholecystitis. Methods A systematic literature search was performed. Risk of bias was assessed using the Newcastle-Ottawa Scale. Random effects model was used to calculate mean difference (MD) in two situations: (a) no cholecystitis versus cholecystitis and (b) simple versus severe cholecystitis. Receiver operating characteristic (ROC) curve analysis was performed to determine cut-off values of NLR for the above situations. Results Ten retrospective studies comprising of 2827 patients were included. 327 had no cholecystitis, 2100 had simple cholecystitis and the remaining 400 had severe cholecystitis. NLR was significantly higher in acute cholecystitis compared to “no cholecystitis” (MD = 8.05 (95% CI 7.71–8.38), p < 0.01) and severe cholecystitis when compared with simple cholecystitis (MD = 3.14 (95% CI 1.26–5.02), p < 0.01). For patients with cholecystitis compared to those without cholecystitis, an NLR cut-off value of 2.98 was identified (AUC = 0.90). Logistic regression analysis confirmed NLR > 2.9 was an independent predictor of cholecystitis (OR 36.0, p = 0.006). In simple versus severe cholecystitis, an NLR cut-off value of 8.5 was identified (AUC = 0.73). Binary logistic regression analysis suggested an NLR > 8.5 was not an independent predictor of severe cholecystitis (OR 6.5 p = 0.090). Conclusion NLR is significantly higher in patients with cholecystitis of any severity compared to patients without cholecystitis. Moreover, NLR can predict acute cholecystitis. However, NLR cannot predict the severity of disease due to inadequately powered studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".