Recruitment strategies for preventive trials. The MAPT study (Multidomain Alzheimer Preventive Trial)
Bibliographic record
Abstract
1680 participants were randomized over the recruitment period in MAPT study. A total of 1290 participants were recruited in the 7 University Hospital centers, and 390 participants in the 6 memory clinics around Toulouse Gerontopole / Alzheimer Disease research clinical center. The first randomization was on May 30, 2008, and the targeted number of randomized participants was reached on February 24, 2011; 2595 subjects were finally screened, of which 1680 fulfilled the eligibility criteria which represents 64.8%. Approximately, one quarter of screened people refused to participate after the detailed presentation of the study and 4.3% were still interested in participating but missed for unknown reasons the baseline visit even after repeated contacts. Of the 1810 subjects who signed the consent for participating to the study at the baseline visit, 130 (7.1%) were excluded because one of the eligibility criteria was not satisfied. Interestingly, the higher percentage of randomizations compared to screened participants is the personal contact source; almost 85 % of screened participants entered in the study. In an equivalent way, Medias and conferences are efficient recruiting sources to enrol volunteers in the study. Unexpectedly, only about 60% of screened participants from the hospital and GP sources were randomized and 33.2% from health care services. Almost a quarter of the randomized participants come from the hospital outpatients clinics and approximately 20% from public conferences. A total of 1128 contacts yielded to 556 screened volunteers and 345 randomized participants in the coordinating center of Toulouse. Thus, 30 % of contacts were recruited.
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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 | 0.090 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".