Predicting Patient Loss to Follow-up in the STABILITY 1 Study
Bibliographic record
Abstract
BACKGROUND: Patients lost to follow-up (LTF) impact even the most meticulously planned randomized controlled trials. Identifying patients at high risk for becoming LTF and employing strategies to retain these patients may reduce attrition bias. METHODS: A cohort of 618 young, active patients undergoing anterior cruciate ligament reconstruction in the STABILITY 1 study was analyzed. Patients completed clinical testing and 9 questionnaires at 3, 6, 12, and 24 months. Multivariable logistic regression was performed for 5 different definitions of LTF. Patient characteristics and study site were included as predictors. RESULTS: The LTF rate was 8.3%. Current or previous smokers (odds ratio [OR] = 2.77; 95% confidence interval [CI]: 1.20 to 5.96), those employed part-time (OR = 2.31; 95% CI: 1.04 to 5.14), and those with body mass index (BMI) of ≥25 kg/m2 had significantly greater odds of becoming LTF compared with nonsmokers, students, and those with BMI of <25 kg/m2, respectively. Those employed part-time were >8 times more likely (95% CI: 2.66 to 26.28) to become LTF compared with students within the first year. Postoperative BMI of ≥25 kg/m2 was significantly associated with 2 times greater odds of missing the in-person clinical examination at any visit or becoming LTF after the first postoperative year. The clinical site was the single largest predictor of missing data at any visit. CONCLUSIONS: Current or previous smoking, part-time employment, and BMI of ≥25 kg/m2 were significant predictors of becoming LTF, and part-time employment was significantly associated with early LTF. BMI of ≥25 kg/m2 was also associated with late LTF and clinical LTF. The clinical site was significantly associated with missing data at any visit. While we cannot accurately predict who will become LTF, investigators should be aware of these factors to identify high-risk patients and focus retention strategies accordingly. CLINICAL RELEVANCE: Understanding factors related to becoming LTF in young, active patients undergoing anterior cruciate ligament reconstruction can help investigators target retention strategies to reduce LTF in studies requiring clinical follow-up in similar populations.
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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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".