IMPACT OF TRAINING LEVEL ON POST-OPERATIVE COMPLICATIONS IN TOTAL KNEE ARTHROPLASTIES: US VERSUS CANADA
Bibliographic record
Abstract
Utilising the (ACS-NSQIP) database, we aimed to evaluate the impact of resident level of training on surgical outcome following (TKA) and to compare the US and Canadian health care training system in regards to 30 days postoperative complications and readmission rates.Using the (CPT) codes we selected from the 2011 and 2012 NSQIP database elective primary TKA with the resident surgeon involved. Of these, all cases with a primary diagnosis code of infection, fracture, mechanical complication, or malignancy and all cases with incomplete or incongruous demographic information were excluded. We also eliminated all the cases with the Attending not present. A total of 2513 cases were included in the study. The cases were stratified into three groups according to the postgraduate level of training {PGY 1 to 3 (junior resident), PGY 4 to 5 (senior resident), and fellow}. Univariate analysis of all patient demographics, comorbidities, intra and postoperative variables, length of surgery, hospital stay and 30 days ...
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".