Predicting completion of follow-up in prospective orthopaedic trauma research
Bibliographic record
Abstract
OBJECTIVE: Orthopaedic trauma studies that collect long-term outcomes are expensive and maintaining high rates of follow-up can be challenging. Knowing what factors influence completion of follow-up could allow interventions to improve this. We aimed to assess which factors influence completion of follow-up in the 12 months following surgery in prospective orthopaedic trauma research. DESIGN: Prospective Cohort Study. SETTING: Level 1 Trauma Center, Vancouver, Canada. PARTICIPANTS: Eight hundred seventy patients recruited to 4 prospective studies investigating the outcomes of operatively treated lower extremity fractures. MAIN OUTCOME MEASUREMENTS: Completion of follow-up defined as completion of all outcome measures at all time points up to 12 months following injury. RESULTS: Univariate analysis and subsequent analysis by building a reductive multivariate regression model allowed for estimation of the influence of factors in completion of follow-up.Eight hundred seventy patients with complete data had previously been recruited and were included in the analysis. Seven hundred seven patients (81.2%) completed follow-up to 12 months. Factors associated with completion of follow up included higher physical component score of SF-36 at baseline, not being on social assistance at the time of injury, being married and having a higher level of educational attainment. CONCLUSIONS: Our study has demonstrated several important factors identifiable at baseline which are associated with a failure to complete follow-up. Although these factors are not modifiable themselves, we advocate that researchers designing studies should plan for additional follow-up resources and interventions for at risk patients. LEVEL OF EVIDENCE: Level IV.
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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 | 0.040 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".