Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it