How do patients want to learn of results of clinical trials? A survey of 1431 breast cancer patients
Bibliographic record
Abstract
Questionnaires were circulated to UK patients and health care professionals (HCPs) participating in the Taxotere as Adjuvant ChemoTherapy (TACT) trial in autumn 2004 asking if and how trial results, when available, should be conveyed to patients. A total of 1431 (37% of surviving UK TACT patients) returned questionnaires. In all, 30 (2%) patients did not want results. In all, 554 (40%) patients preferred to receive them via their hospital; 664 (47%) preferred results posted directly to their home, 177 (13%) preferred a letter providing a telephone number to request results. Six hundred and twelve patients thought results should come directly from the trials office. One hundred and seventy-six HCPs from 89 UK centres (86%) returned questionnaires. In all, 169 out of 176 patients (96%) thought results should be written in lay terms for patients. Seventy (41%) preferred patients to receive results via their hospital; 64 (38%) preferred a letter providing a telephone number to request results, and 32 (19%) preferred results posted directly to patients. Thirty-one HCPs (18%) thought results to patients should come directly from the trials office. A total of 868 (61%) patients thought next of kin of deceased patients should receive results, 543 (38%) did not; 47 (27%) HCPs thought they should; 118 (68%) did not.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".