Loss to follow-up: initial non-responders do not differ from responders in terms of 2-year outcome in a hip arthroscopy registry
Bibliographic record
Abstract
Abstract Loss to follow-up in registry studies is a problem due to potential selection bias. There is no consensus on the effect of response rate. The aim of this study was to compare patient-reported outcome measures (PROMs) between responders and initial non-responders (INR) in a hip arthroscopy registry and to examine whether demographics affect the response rate. Data from hip arthroscopies performed at two centres in Gothenburg were collected and the patients were followed up with PROMs. The follow-up was a minimum of 2 years after surgery. All 536 patients who underwent primary hip arthroscopies during 2015 and 2016 and had recorded pre-operative PROMs were included. A total of 396 patients completed the follow-up and were labelled ‘Responders’ (R) and 107 patients responded after reminders were sent and labelled ‘Initial non-responders’ (INR). The mean time of follow-up was 24.7 ± 2.9 and 42.5 ± 7.0 months for the R- and INR-group, respectively. There were no differences between the two groups at the follow-up for the Copenhagen Hip and Groin Outcome Score, European Quality of life 5 dimensions questionnaire, EQ-VAS, International Hip Outcome Tool or a visual analogue scale for hip function. A larger proportion of R was satisfied after hip arthroscopy compared with INR (86% versus 70%, P = 0.0003). INR were younger than responders (31.5 ± 12.5 versus 35.6 ± 12.7 years of age). The conclusion of the study was that there were no differences between R and INR at the follow-up across the PROMs except patient satisfaction, where responders were more satisfied.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".