Mid-term Patient-reported Outcomes of Hip Arthroplasty After Previous Hip Arthroscopy: A Matched Case-control Study With a Minimum 5-year Follow-up
Bibliographic record
Abstract
BACKGROUND: Previous hip arthroscopy may affect the outcomes of subsequent hip arthroplasty. The purpose is to compare mid-term patient-reported outcomes (PROs) and complication rates in patients who had previous ipsilateral hip arthroscopy (PA) with those without a previous surgery. METHODS: A minimum 5-year PROs, complications, and revision surgery rates were compared between total hip arthroplasty (THA) recipients who received PA and those without. Available intraoperative findings, procedures, and conversion time of arthroscopies were reported. The relative risk (RR) of complications and revision THAs were reported. A Kaplan-Meier analysis assessed survivorship of revision THA. RESULTS: There were 34 cases (33 patients) of PA that were matched to 89 control cases (87 patients). Both cohorts reported similar scores for Harris hip score, Forgotten Joint Score, pain, and patient satisfaction. No differences in the outcomes were found based on the arthroplasty approach. A higher postoperative complication rate {RR, 2.617 (95% confidence interval [CI], 0.808 to 8.476)} and revision THA rate (RR, 13.088 [95% CI, 1.59 to 107.99]) were found in the PA group. CONCLUSION: Patients with PA demonstrated similar levels of PROs as those without previous ipsilateral hip arthroscopy. There may, however, be a higher rate of complications and revision surgery in the PA group. LEVEL OF EVIDENCE: III.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, 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".