Loss to follow-up after total hip replacement: a source of bias in patient reported outcome measures and registry datasets?
Bibliographic record
Abstract
Patient reported outcome measures (PROMs) are used to gauge clinical performance. The PROMs outcome programme at our centre achieves a preoperative data capture rate of 99%. This falls to 90.6%, 89%, 83% and 79% at the six-week, six-month, one-year and two-year time points, respectively. The study aims were to determine factors associated with patients who did not respond to outcome questionnaires following total hip replacement (THR), and the potential implications this may have when assessing patients following THRs. During the first year of the PROMs programme, 1,322 patients underwent unilateral primary THR at our institution. Of these, 1,311 completed preoperative questionnaires. Thirty-eight patients (2.9%) died within two years of surgery and have been excluded. For the remaining 1,273 patients, we identified those who did not return postoperative questionnaires at each of our review time points. Younger age, lower baseline EQ5D and Oxford Hip scores (OHS) were significantly associated with non-response (p<0.001). Patients with lower satisfaction scores, OHS and EQ5D scores, were less likely to respond to subsequent questionnaires. A significant association between non-response and deprivation (p<0.001) was demonstrated. Our findings suggest that the more satisfied patients are over-represented and our reported outcome results are better than they would have been if all patients had responded. This phenomenon may apply to studies where those categorised as "lost to follow-up" represent a subset of patients who have disengaged due to poor outcome or satisfaction.
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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 | 0.392 | 0.588 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".