Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: Identification of groups of patients following similar trajectories of time-varying patient characteristics are often of considerable clinical value. This study provides an example of how the identification of trajectory groups of patients can be useful. METHODS: Using clinical and administrative data of a prospective cohort study aiming to improve the secondary prevention of osteoporosis-related fractures with a Fracture Liaison Service (FLS), trajectory groups for visit compliance over time (2-year follow-up) were predicted using group-based trajectory modeling. Predictors of trajectory groups were identified using multinomial logistic regressions. RESULTS: Among 532 participants (86% women, mean age 63 years), three trajectories were identified and interpreted as high followers, intermediate followers, and low followers. The predicted probability for group-membership was: 48.4% high followers, 28.1% intermediate followers, 23.5% low followers. A lower femoral bone mineral density and polypharmacy were predictors of being in the high followers compared to the low followers group; predictors for being in the intermediate followers group were polypharmacy and referral to a bone specialist at baseline. CONCLUSIONS: Results provided information on visit compliance patterns and predictors for the patients undergoing the intervention. This information has important implications when implementing such health services and determining their effectiveness.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".