The Value of C-Reactive Protein in Enhancing Diagnosis of Acute Appendicitis
Bibliographic record
Abstract
Background: Acute appendicitis is one of the most common surgical emergencies. Accurate diagnosis of acute appendicitis is based on careful history, physical examination, laboratory and imaging findings. The aim of this study was to analyze the role of C-reactive protein (CRP) in improving the accuracy of diagnosis of acute appendicitis and to compare it with the histopathology findings. Methods: A retrospective study of 100 patients aged between 7 and 69 years who presented to the A&E in 2013 - 2014, in whom the diagnosis of appendicitis was the attending physician ’s primary consideration, was conducted. Measures included age, gender, initial CRP counts, and discharge diagnosis. Based on histology, appendicitis was classified as simple (inflammation) or complicated suppurative, gangrenous, necrotizing perforation. Results: Out of 100 patients, 32% were classified as an inflamed appendicitis. Of the patients, 34% were shown to have suppurative appendicitis, 17% gangrenous, 13% perforated, and 5% necrotizing. Very high CRP is likely to be associated with necrotizing appendicitis, while CRP of 40 or more can be associated with suppurative or inflammatory one. CRP more than 100 and less than 150 may suggest possible perforated or gangrenous appendicitis. Conclusion: Our data provided provisional evidence that very high CRP may be related to necrotizing appendicitis, while CRP above 40 mg/L may suggest suppurative or inflammatory appendicitis. J Curr Surg. 2017;7(1-2):7-10 doi: https://doi.org/10.14740/jcs316w
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".