The Impact of Resident Involvement on Surgical Outcomes following Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
Abstract Hands-on participation in the operating room (OR) is an integral component of surgical resident training. However, the implications of resident involvement in many orthopaedic procedures are not well defined. This study aims to assess the effect of resident involvement on short-term outcomes following anterior cruciate ligament reconstruction (ACLR). The National Surgical Quality Improvement Program (NSQIP) database was queried to identify all patients who underwent ACLR from 2005 to 2012. Demographic variables, resident participation, 30-day complications, and intraoperative time parameters were assessed for all cases. Resident and nonresident cases were matched using propensity scores. Outcomes were analyzed using univariate and multivariate regression analyses, as well as stratified by resident level of training. Univariate analysis of 1,222 resident and 1,188 nonresident cases demonstrated no difference in acute postoperative complication rates between groups. There was no significant difference in the incidence of overall complications based on resident level of training (p = 0.109). Operative time was significantly longer for cases in which a resident was involved (109.5 vs. 101.7 minutes; p < 0.001). Multivariate analysis identified no significant predictors of major postoperative complications, while patient history of chronic obstructive pulmonary disease was the only independent risk factor associated with minor complications. Resident involvement in ACLR was not associated with 30-day complications despite a slight increase in operative time. These findings provide reassurance that resident involvement in ACLR procedures is safe, although future investigations should focus on long-term postoperative outcomes.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".