Centrale coördinatie van een multicenter studie als alternatief voor betaling per patient: de ervaring bij de HEALTH-trial
Bibliographic record
Abstract
ABSTRACT \n \nObjective: Multicenter clinical trials can be organized in different ways. Multiple centers participated in the HEALTH trial (Hip Fracture Evaluation with ALternatives of Total Hip Arthroplasty versus Hemi-Arthroplasty). For the Dutch sites most study tasks are managed by a central trial coordinator, whereas Canadian and US sites use local study coordinators. The aim of this study was to analyze how these strategies affected trial performance. \nDesign: Prospective observational study. \nMethod: Data related to ethics approval, trial start-up time, inclusion rate and percentage of completed follow-ups were collected for each hospital and compared. Data from pre-trial screening were compared with actual inclusion rates. \nResults: The median start-up time of the trial after obtaining ethics approval was shorter in the Netherlands than in Canada and the US (4.6 versus 11.6 weeks). The inclusion rate was similar in both groups (0.62 versus 0.64/month). The median percentage of enrolled patients in the Netherlands was 27.3% versus 17.0% in Canada/US. The actual inclusion rates were lower than expected from pre-trial screening. The percentage of effectuated follow-up visits was >90% in both groups. \nConclusion: In this study, central trial coordination contributed to faster trial start-up and higher inclusion rates, but had no effect on the effectuated follow-up visits. Central coordination is therefore a suitable alternative for appointing these tasks to local research assistants and per patient payment. Central coordination enables non-academic hospitals to participate in clinical trials. Limiting conditions for central coordinat
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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 | 0.298 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".