Understanding decision supports (DS) for women with breast cancer eligible for clinical trials (CT)
Bibliographic record
Abstract
10534 Background: CTs are vital to the development of treatments for patients with cancer, but a low proportion of patients participate in trials, resulting in decreased access to new options. Methods: The study purpose was to explore DS for CT participation decision-making with women diagnosed with breast cancer who were offered a CT. 31 women took part in 6 focus groups - 3 groups of women who consented to a CT, 3 groups who declined a CT. Open-ended questions were asked about specific DS and ideas for new ones. Information rich cases were selected for the sample. Data analyses were conducted by 2 independent coders using a line-by-line, open coding process. Reliability was checked by a 3rd coder. Data was organized with template and editing approaches. Results were compared by group type (declined/consented to CT). Results: Common themes emerged from both group types: too much information is given at the first oncology consult; patients prefer to get CT information from the cancer centre, after their surgery, but prior to their oncology consult; no strong preference about who acts as a DS—family doctor, surgeon, other—as long as good relationship exists; oncologist (to lesser degree surgeon) is seen as most informed about their case; preference for oncologist vs trials nurse to describe CT concept, answer questions, direct them to other information sources; patients doubt family doctors or surgeons have detailed knowledge of CTs, know specific trial data; patients want to feel prepared, know what may happen before they come to oncologist - consult process, CT may be option - to avoid surprise; helpful to know that there is time to make CT decision; other patients are a good source of DS and information. Conclusions: Patients had strong preference to receive information about CTs prior to their consultation with an oncologist; this timing was seen as helpful for decision-making about a CT by both group types. No significant financial relationships to disclose.
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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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".