Loss of Follow-Up in Orthopaedic Trauma
Bibliographic record
Abstract
BACKGROUND: Loss of follow-up represents a potential source of bias. Suggested guidelines propose 20% loss of follow-up as acceptable. However, these guidelines have not been established through scientific investigations. The goal of this study was to evaluate how loss of follow-up influences the statistical significance in a trauma database. METHODS: A database of 637 polytrauma patients with an average follow-up of 17.5 years postinjury was used. The functional outcome of workers' compensation patients versus nonworkers' compensation patients was compared using a validated scoring system. A significant difference between the 2 groups was found (P < 0.05). We simulated a gradually increasing loss of follow-up by randomly deleting an increasing number of patients from 2%, 5%, and 10%, and then increasing in increments of 5% until the significance changed. This process was repeated 50 times, each time with a different electronic random generator. For each simulation series, we documented at which simulated loss of follow-up that the results turned from significant (P < 0.05) to nonsignificant (P > 0.05). RESULTS: Among 50 simulation series, the turning point from significant to nonsignificant varied between 15% and 75% loss of follow-up. A simulated loss of follow-up of 10% did not change the statistical significance in any of the simulation series; a simulated loss of follow-up of 20% changed the statistical significance in 28% of our simulation series. CONCLUSIONS: A loss of follow-up of 20% or less may frequently change the study results. Researchers should establish protocols to minimize loss of follow-up and clearly state the loss of follow-up in manuscript publications.
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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 | 0.042 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".