The Impact of Minimally Invasive Surgery on Residents' Open Operative Experience
Bibliographic record
Abstract
In Brief Summary: Over the past 2 decades, the operative experience of surgical residents has undergone major changes due to advances in the science and technology of surgery, treatment modality, growth of subspecialties, work hour regulations, and an emphasis on shorter hospitalization. Method: We performed a comprehensive statistical analysis of national data from ACGME (1998–2008), with a focus on changes in the component operations. Results: Since 1993, when minimally invasive surgery was first recorded in ACGME data base, the US residents' open operative experience began to register a continuing decline. Today, a quarter of the resident's operations are closed procedures. During the same period, trauma operative experience has decreased by 50%. If the decline in open operations continues at this rate, within 10 years it will drop to less than 60% of that in 1993. Gastrointestinal (especially biliary) operations are the most affected. Changes in ACGME data format of component operations made it difficult to determine the effect of work hour limit by looking at total operations alone. Conclusion: Training in open operative surgery, the foundation of the craft of surgery, is on the decline. Lack of operative trauma hurts intra-operative crisis management and decision making. These deficiencies deserve educational effort at a higher priority than accorded so far. The national resident operative statistics over the past 20 years documented a progressive decline of open operative experience, simultaneous with a steady increase in minimally invasive surgery. If the craft of traditional surgery is to be preserved, resident training must be directed to make up for the deficit.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".