Surgeon‐Specific Traction Time During Hip Arthroscopy for Primary Labral Repair Can Continue to Decrease After a Substantial Number of Surgeries
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the total traction time and traction time as a function of anchors placed (TTAP) for primary labral repair in patients undergoing hip arthroscopy by a single surgeon. METHODS: Patients were included if they received a primary labral repair with or without acetabuloplasty, chondroplasty, or ligamentum teres debridement as part of the treatment for femoroacetabular impingement (FAI). Patients were excluded if they had a previous ipsilateral hip surgery, prior hip conditions, Tönnis grade >1, open procedures, microfracture, ligamentum teres reconstruction, or labral reconstruction. TTAP was calculated by dividing total traction time by the number of anchors placed. RESULTS: , respectively. A total traction time of 60 minutes was first achieved after 268 cases. Mean overall total traction time was 58.16 minutes (95% CI [57.35, 58.97]) and mean TTAP was 16.24 minutes (95% CI [15.93,16.55]) after 2,350 cases. Total traction time plateaued after 374 cases at 55.92 minutes, while TTAP plateaued after 487 cases at 14.93 minutes. CONCLUSION: Surgeons who introduce hip arthroscopy into their practice can expect to see improvements in traction time during the first 500 surgeries performed, as total traction time plateaued after 374 cases and TTAP plateaued after 487 cases. LEVEL OF EVIDENCE: IV: case series.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".