Neoplasm or Malignancy Involving the Appendix Is Seven Times More Likely for Elective vs Emergency Appendectomies in Cases without a Preoperative Neoplasm Diagnosis: A Review of >52,000 ACS NSQIP Cases
Bibliographic record
Abstract
Introduction: Elective appendectomy is performed for treatment of preoperatively diagnosed or suspected neoplasms, and after missed appendicitis initially managed non-operatively. This study aimed to investigate differences in neoplasm rates between elective and emergency appendectomies. Methods: The ACS NSQIP Appendectomy database was analyzed from 2016-2019. Cases were divided into elective and emergent. Primary outcome was rate of final pathologic diagnosis of tumor or malignancy involving the appendix. Cases were excluded if the preoperative diagnosis included any neoplasm. Cases with conflicting indicators for elective and emergency were excluded from subgroup analysis. Results: There were 52,559 appendectomies, of which 828 cases (1.6%) with preoperative diagnosis of neoplasm were excluded, leaving 51,731 appendectomies in the study population. There were 4,475 (8.7%) elective and 31,828 (61.5%) emergent appendectomies. The incidence of final pathologic diagnosis of tumour or malignancy involving the appendix was 0.9% (463 cases) for all appendectomies, 0.6% (177 cases) for emergencies, and 3.7% (166 cases) for elective appendectomies. This represents a 667.0% relative risk and 3.2% absolute risk increase for elective compared withemergency appendectomies, with an odds ratio of 6.88 (95% confidence interval 5.6-8.5). Chi-square test with 95% confidence level was statistically significant (p<0.001) for differences between all 3 groups. Conclusion: Neoplasm or malignancy involving the appendix is seven times more likely for elective than emergency appendectomies. A higher index of suspicion for cancer should be maintained for patients undergoing elective appendectomy. Interval appendectomy or colonoscopy should be selectively considered in patients with missed appendicitis initially managed non-operatively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".