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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".