Microfracture for knee chondral defects: a survey of surgical practice among Canadian orthopedic surgeons
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe the practice of microfracture surgery for knee chondral defects among Canadian orthopedic surgeons. METHODS: All orthopedic surgeon members of the Canadian Orthopaedic Association were invited to participate in a survey, designed to explore the microfracture technique used by orthopedic surgeons in the treatment for knee chondral defects The primary outcome measure was an emailed 26-item questionnaire, which explored indications for microfracture surgery, surgical techniques, types of postoperative rehabilitation regimes used and assessment of outcome. In addition, responses were compared between orthopedic surgeons with a sports medicine practice to surgeons with a non-sports medicine practice. RESULTS: The survey response rate was 24.6% (299/1,216), with 131 regularly performing microfracture. 41% of surgeons indicated that they had no upper limit for age at the time of surgery, and 87% indicated no upper limit for body mass index. The majority of respondents (97%) resected cartilage back to a stable margin, while 69% of respondents removed the calcified cartilage layer prior to creating holes. Only 11% of respondents used continuous passive motion (CPM) postoperatively, and 39% did not restrict weight bearing. Sports surgeons were more likely than non-sports surgeons to remove the calcified cartilage layer, use a 45° pick, use CPM and restrict weight bearing postoperatively (all P values < 0.05). CONCLUSIONS: This survey on microfracture for knee chondral defects revealed widespread variation among surgeons regarding the indications for surgery, surgical technique, postoperative rehabilitation and assessment of outcome. Sports surgeons demonstrate better evidence-based practice than non-sports surgeons for a few important parameters. LEVEL OF EVIDENCE: Cross-sectional survey, Level II.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".