Quality of inguinal hernia operative reports: room for improvement
Bibliographic record
Abstract
BACKGROUND: Operative reports (ORs) serve as the official documentation of surgical procedures. They are essential for optimal patient care, physician accountability and billing, and direction for clinical research and auditing. Nonstandardized narrative reports are often of poor quality and lacking in detail. We sought to audit the completeness of narrative inguinal hernia ORs. METHODS: A standardized checklist for inguinal hernia repair (IHR) comprising 33 variables was developed by consensus of 4 surgeons. Five high-volume IHR surgeons categorized items as essential, preferable or nonessential. We audited ORs for open IHR at 6 academic hospitals. RESULTS: We audited 213 ORs, and we excluded 7 femoral hernia ORs. Tension-free repairs were the most common (82.5%), and the plug-and-patch technique was the most frequent (52.9%). Residents dictated 59% of ORs. Of 33 variables, 15 were considered essential and, on average, 10.8 ± 1.3 were included. Poorly reported elements included first occurrence versus recurrent repair (8.3%), small bowel viability in incarcerated hernias (10.7%) and occurrence of intraoperative complications (32.5%). Of 18 nonessential elements, deep vein thrombosis prophylaxis, preoperative antibiotics and urgency were reported in 1.9%, 11.7% and 24.3% of ORs, respectively. Repair-specific details were reported in 0 to 97.1% of ORs, including patch sutured to tubercle (55.1%) and location of plug (67.0%). CONCLUSION: Completeness of IHR ORs varied with regards to essential and nonessential items but were generally incomplete, suggesting there is opportunity for improvement, including implementation of a standardized synoptic OR.
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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 | 0.142 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".