Intraoperative traction has a negligible time-dependent influence on patient-reported outcomes after hip arthroscopy: a cohort study
Bibliographic record
Abstract
ABSTRACT The aim of this study is to determine if post-operative patient-reported outcome measures (PROMs) are influenced by hip arthroscopy traction duration. Patients from a local prospective hip arthroscopy database were retrospectively analyzed. Four hip-specific PROMs were utilized: modified Harris Hip Score (mHHS), Hip Outcome Score—Activities of Daily Living (HOS-ADL), Hip Outcome Score—Sports Specific (HOS-SS), and international Hip Outcome Tool (iHOT). PROMs were collected pre-operatively and 6 months, 1 year and 2 years post-operatively. Two cohorts were created based on a cut-off corresponding to the 66th percentile for our patient cohort. Analyses were completed for each PROM at each post-operative interval with univariable statistics. Multivariable statistics were examined to identify the variables that were predictive of achieving post-operative minimal clinically important difference (MCID) at the 2-year follow-up. Overall, 222 patients met the inclusion criteria. The mean age was 32.4 ± 9.4 years, and 116 (52.3%) were female. The average traction time of the study population was 46.1 ± 12.9 min. A total of 145 patients were included in the short traction cohort (65%) with traction times of <50 min (66th percentile). No significant differences were found regarding PROM scores or MCID achievement rates between both cohorts at any post-operative period. In multivariable analyses, achievement of MCID was predicted by a decrease in traction time for all PROMs and pincer-type resection for mHSS, HOS-ADL and iHOT. There was no difference in PROMs and MCID achievement between longer and shorter traction time cohorts. On multivariable analysis, a decrease in traction time is predictive of MCID for all PROM scores and pincer-type resection was predictive of MCID for most PROM scores. Level of evidence: Level III, cohort study
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".