Predictors of Loss to Follow-up in Hip Fracture Trials: A Secondary Analysis of the FAITH and HEALTH Trials
Bibliographic record
Abstract
BACKGROUND: Hip fracture trials often suffer substantial loss to follow-up due to difficulties locating and communicating with participants or when participants, or their family members, withdraw their consent. We aimed to determine which factors were associated with being unable to contact FAITH and HEALTH participants for their 24-month follow-up and to also determine which factors were associated with their withdrawal of consent. METHODS: We conducted 2 multivariable logistic regression analyses to determine which factors were predictive of being unable to contact participants at 24 months postfracture and withdrawal of consent within 24 months of their fracture. Results were reported as odds ratios, 95% confidence intervals, and associated P-values. All tests were 2-tailed with alpha = 0.05. RESULTS: We were unable to contact 123 of 2520 participants (4.9%) for their 24-month follow-up visits and 124 (4.9%) withdrew their consent from the trial. Being non-White (P = 0.003), enrolled from a non-European hospital (P < 0.001), and treated with arthroplasty (P < 0.001) were associated with an increased odds of not completing the 24-month follow-up visit. Being enrolled from a hospital in the United States (P = 0.02), from a hospital in Oceania, India, or South Africa (P < 0.001) as compared to a European hospital, and treated with arthroplasty (P < 0.001) were associated with an increased odds of consent withdrawal. DISCUSSION: Certain factors may be predictive of loss to follow-up in hip fracture trials. We suggest that the identification of such factors may be used to inform and improve retention strategies in future orthopaedic hip fracture trials. LEVEL OF EVIDENCE: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.041 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".