Systematic Review of Learning Curves for Minimally Invasive Abdominal Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine how minimally invasive surgical learning curves are assessed and define an ideal framework for this assessment. BACKGROUND: Learning curves have implications for training and adoption of new procedures and devices. In 2000, a review of the learning curve literature was done by Ramsay et al and it called for improved reporting and statistical evaluation of learning curves. Since then, a body of literature is emerging on learning curves but the presentation and analysis vary. METHODS: A systematic search was performed of MEDLINE, EMBASE, ISI Web of Science, ERIC, and the Cochrane Library from 1985 to August 2012. The inclusion criteria are minimally invasive abdominal surgery formally analyzing the learning curve and English language. 592 (11.1%) of the identified studies met the selection criteria. RESULTS: Time is the most commonly used proxy for the learning curve (508, 86%). Intraoperative outcomes were used in 316 (53%) of the articles, postoperative outcomes in 306 (52%), technical skills in 102 (17%), and patient-oriented outcomes in 38 (6%) articles. Over time, there was evidence of an increase in the relative amount of laparoscopic and robotic studies (P < 0.001) without statistical evidence of a change in the complexity of analysis (P = 0.121). CONCLUSIONS: Assessment of learning curves is needed to inform surgical training and evaluate new clinical procedures. An ideal analysis would account for the degree of complexity of individual cases and the inherent differences between surgeons. There is no single proxy that best represents the success of surgery, and hence multiple outcomes should be collected.
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.
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 | 0.036 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".