Microfracture for chondral defects: assessment of the variability of surgical technique in cadavers
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to assess the variability of the microfracture technique when performed by experienced knee arthroscopy surgeons. METHOD: Four surgeons were each asked to perform microfracture on six preformed cartilage defects in fresh human cadaveric knees. Surgeons were instructed on penetration depth, inter-hole distance, and to place the holes perpendicular to the subchondral surface. Micro-computed tomography was used to calculate depth error, inter-hole distance error, and deviation of penetration angles from the perpendicular. RESULTS: All surgeons misjudged depth and inter-hole distance, tending to make microfracture holes too deep (depth error 1.1 mm ± 1.9) and too close together (inter-hole distance error: -0.8 mm ± 0.4). Fifty-one per cent of holes were angled more than 10° from the perpendicular (range 2.6°-19.8°). Both depth and distance errors were significantly lower in the trochlear groove than on the femoral condyle (p < 0.05). Surface shearing was associated with both penetration depth >4 mm and angles >20°. Inter-hole infraction occurred in holes closer than 2.5 mm to each other. CONCLUSION: Even experienced knee arthroscopy surgeons demonstrate inconsistency in surgical technique when performing microfracture. While further research will be required to demonstrate that these variations in surgical technique are associated with poorer clinical outcomes after microfracture, surgeons should attempt to minimizing such variations in order to prevent surface shearing and inter-hole infraction.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".