Recent experience with laparoscopic appendectomy in a Canadian teaching centre.
Bibliographic record
Abstract
OBJECTIVE: Laparoscopic appendectomy (LA) remains controversial in our city, in part because of results obtained early in the learning curve. In 1995, our centre reported that LA took 30 minutes longer and yet resulted in similar length of hospital stay, compared with open appendectomy (OA). The purpose of the current study is to reexamine LA now that more experience has been gained with the procedure and to document the learning curve at a typical Canadian teaching centre. METHODS: We undertook a retrospective chart review of patients undergoing nonelective appendectomy between January 2001 and June 2004; this yielded 201 charts that satisfied the inclusion criteria. The 201 cases were divided into 3 groups, each consisting of 67 consecutive cases, to allow for comparison between early, middle and late experience. The main outcomes of interest were operative time, length of stay and the changes over time that occurred in these 2 measures. An intent-to-treat analysis was performed. RESULTS: The mean operative time differed by only minutes: 54.9 (standard error of mean [SEM] 1.9) minutes for LA and 48.8 (SEM 1.4) minutes for OA (p = 0.004). Length of stay was 1.3 (SEM 0.1) days and 2.9 (SEM 0.2) days for LA and OA groups, respectively (p 0.0001). Analysis of the 3 time periods (early, middle and late) revealed significant improvements in operative time and length of stay for LA in the middle, compared with the early, time periods. CONCLUSION: These data suggest that, with experience, LA operative time approaches that of OA and length of stay decreases. A shortened hospital stay and similar operative time, along with educational advantages, support the use of the LA in teaching centres.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".