Redenen voor patiënten om (niet) te participeren in klinische trials; een systematisch literatuuroverzicht
Bibliographic record
Abstract
OBJECTIVE: To assess the factors that may influence a patient's consent to participate in a clinical trial. DESIGN: Systematic literature survey. METHOD: Studies on the characteristics of patients, trials, the physicians requesting informed consent and the informed consent procedures were looked for in Medline, Embase, and Cinahl. Articles published in English, German, Dutch or French in the period 1980-2002 and originating in Europe, the United States, Canada, New Zealand or Australia were included. Studies on non-adults, healthy experimental subjects or less than 30 patients were excluded. RESULTS: Thirty suitable studies were retrieved. Factors that may affect the granting of consent to participate in a clinical trial included: uncertainty of the patient, randomisation and the use of a placebo, the relationship between the person asking for informed consent and the patient, and the dissemination of information during the informed consent procedure. Since these factors are often interrelated, no single factor could be identified as decisive for participation in a clinical trial; they can influence the decision of the patient to participate in a trial in either a positive or a negative direction. CONCLUSION: Optimalization of the information concerning informed consent, the way the information is provided and the attitude of the person requesting informed consent are important and sometimes decisive factors that may determine the participation process
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.
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 | 0.394 | 0.633 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".