Quantitative Analysis of Factors of Attrition in a Double-blind rTMS Study for Alzheimer Treatment
Bibliographic record
Abstract
Attrition is a particular concern in studies examining the efficacy of a treatment for Alzheimer disease. Analyzing reasons for withdrawal in Alzheimer studies is crucial to ruling out attrition bias, which can undermine a study's validity. In contrast, attrition in studies using repetitive transcranial magnetic stimulation (rTMS) has received much less attention. Our goal was to identify any commonalities between participants who withdrew for the same reasons. Three independent coders rated each response concerning the reasons for withdrawal, and frequency tables were generated to characterize the participants within each category. This study was conducted on the 28 withdrawn cases from a 7-month study investigating the short-term and long-term therapeutic effects of rTMS for Alzheimer disease among 156 participants across 3 sites of the study. Seven reasons for withdrawal were identified, with health and medical changes being the most commonly reported reason (7 participants). Personal issues involving family or caregivers were the next most common (5 participants), and the remaining 5 categories consisted of 3 participants each. Although the limited sample size prevented the use of inferential statistics, our findings highlight the need for more transparent reporting of attrition rates and withdrawal reasons by rTMS researchers.
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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 | 0.320 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".