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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".