Evaluation of factors associated with recruitment in hematological clinical trials: a retrospective cohort study
Bibliographic record
Abstract
The objectives of this study were to measure the recruitment, to study characteristics associated with recruitment, and to explore reasons for non-recruitment in clinical trials for malignant hematological diseases. Trials opened between 2002 and 2008 were selected. If the patient fulfilled the main criteria of the protocol, all eligibility criteria of the protocol were assessed. A total of 1394 patients-protocol were identified in 17 protocols (697 patients, since a patient could have been eligible for more than one protocol) and 195 patients-protocol (186 patients) of these fulfilled the main criteria of the protocol. Among the 195 patients-protocol, 133 (68·2%) fulfilled all the eligibility criteria and 45 (23·1%) were recruited. Patients, physicians, and protocol characteristics were not associated with recruitment. The most common reasons for not being recruited were as follow: 40·7%, not fulfilling all eligibility criteria; 31·3%, protocol not being proposed according to the chart; and 22·7%, patients' refusal.
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 | 0.074 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".