CUSUM analysis of J-pouch surgery reflects no learning curve after board certification.
Bibliographic record
Abstract
OBJECTIVE: To investigate changes in morbidity and mortality associated with ileal J-pouch surgery performed during the first 3 years of a single surgeon's practice to determine the presence or absence of a learning curve after fellowship training. METHODS: From July 2002 to July 2005, an observational study of postoperative outcomes was undertaken, in which 30-day and inhospital morbidity and mortality were assessed. A total of 37 patients (17 women and 20 men) underwent the surgery; their average age was 32 (range 16-51) years. The operation was performed for ulcerative colitis n = 31), familial adenomatous polyposis n = 4) and indeterminate colitis n = 2); 32 were diverted and 5 were not. Predicted morbidity and mortality were 31.66% and 1.47%, respectively. Observed morbidity and mortality were 29.7% and 0%, respectively. I used a risk-adjusted cumulative sum (CUSUM) model to compare observed outcomes with predicted outcomes according to a validated scoring system and to analyze outcomes with adjusting for risk on a case-by-case basis. RESULTS: CUSUM analysis revealed a flat curve trending down over the duration. CONCLUSION: CUSUM methodology permits documentation of quality control during the first 3 years of practice. The experience of a single board-certified colorectal surgeon reveals acceptable results in the first 3 years of practice, with no obvious learning curve. The results suggest that fellowship training and board certification conferred reasonable proficiency in J-pouch surgery before the onset of practice.
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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 | 0.004 | 0.032 |
| 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 | 0.000 | 0.001 |
| 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".