Lost but not forgotten: patients lost to follow-up in a trauma database.
Bibliographic record
Abstract
OBJECTIVES: To determine the characteristics of patients lost to follow-up and to identify if they are significantly different from those who are followed up in the context of a prospective randomized controlled trial. DESIGN: A retrospective review of a prospectively acquired trauma database. SETTING: A level 1 university-affiliated trauma hospital. PATIENTS: Two hundred and thirty-six patients treated for displaced intra-articular calcaneal fractures between April 1991 and December 1996. Of these, 198 were catcgorized as "attenders" and the remaining 38 were deemed "nonattenders." Demographics, severity of injury, intervention and post-treatment status of the 2 groups were compared. Demographic information, including age, gender, occupation workload, Workers' Compensation Board involvement and other standard trauma information were compared and the differences analyzed. RESULTS: The nonattenders were younger than the attenders, and there was a significantly increased proportion of Aboriginal Canadians in the nonattenders group. Attenders were more likely to be "skilled or semi-skilled clerical, sales, service or trades crafts" workers, and nonattenders were more likely to be "unskilled clerical, sales, service or labour" workers. Attenders were more likely to have a preoperative Bohler's angle of < 0 degrees, compared with a preoperative Bohler's angle of 0 degrees to 15 degrees for nonattenders. CONCLUSIONS: This trauma population is at higher risk of being marginalized by society and may not have the same accessibility to a study nurse or a hospital contact person. Patients lost to follow-up are a demographically and clinically different patient population from those who remain involved in a long-term prospective trauma study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".