A study on the diagnostic and predictive value of neutrophil-to-lymphocyte ratio for early perforation in acute appendicitis
Bibliographic record
Abstract
Objective To investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) for early perforation of the appendix in patients with acute appendicitis (AA). Methods Two hundred and 80eighty patients with the first episode of AA within 48 hours and not yet treated with antibiotics were included in this study retrospectively; divided into two groups by the presence or absence of perforation. The clinical data, traditional inflammatory indexes, and NLR were compared between the two groups. The predictive value of inflammatory indexes on the early appendiceal perforation was explored. Results Patients in the perforated group were older, with higher blood WBC, NEU% and NLR, lower LYM% and PLT, higher serum CRP and PCT, and longer hospitalization time. Multivariate regression analysis revealed that high WBC, NEU%, NLR, CRP, PCT, and low LYM% were independent risk factors for early appendiceal perforation. The ROC curves revealed that the predictive value of WBC, CRP, and PCT for early appendiceal perforation was low. NLR, LYM%, and NEU% had a higher predictive value, with AUC values of 0.947, 0.928, and 0.920, respectively. NLR had the highest predictive value. The diagnostic Cut-off value of NLR is 10.83 with a sensitivity of 0.963, and a specificity of 0.850. Conclusion NLR can be used as an effective and sensitive predictive maker of early appendiceal perforation in AA patients. It is easy to generate from existing routine clinical laboratory testing for AA and can be included in complete blood count (CBC) as a routine or add-on value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".