Bibliographic record
Abstract
BACKGROUND: New surgical techniques should be formally evaluated for feasibility and safety. As a model for this evaluation, this study examines the authors' institution's experience with splenectomy for benign and malignant hematologic disease since the introduction of laparoscopic splenectomy (LS) in 1996. The authors present the evaluation of the recognized surgeon/institutional learning curve using CUSUM (cumulative sum) analysis. METHODS: This is a single institution retrospective chart review of consecutive splenectomies for hematologic disease performed between 1996 and 2008. The primary outcome was conversion to open splenectomy. The learning curve for LS was evaluated using CUSUM analysis. RESULTS: A total of 123 splenectomies were performed for benign (51.2%) or malignant (48.7%) hematologic disease. 58% of patients underwent planned LS, with a 21% conversion rate. The surgeon's overall learning curves for LS, as well as that for malignant disease, were maintained within acceptable conversion thresholds. However, the learning curve for benign disease did cross the unacceptable conversion threshold at case 29. With additional experience, the curve again approached the acceptable conversion threshold. Patients with malignant disease were significantly older (P = .0004), had larger spleens (P = .0004), were more likely to undergo open splenectomy (P = .001), and had longer lengths of stay (P = .01). However, there was no significant difference in operative time, transfusion requirements, morbidity rates, or mortality rates between patients with benign and malignant disease. CONCLUSION: LS, for benign or for malignant hematologic disease, is associated with a significant learning curve. This evaluation model illustrates that careful patient selection and ongoing quality assessment is essential when introducing a new technique.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".