Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it