What role do cancer patients want to play in treatment decision making: A pooled-analysis
Bibliographic record
Abstract
8521 Background: The extent of patient involvement in the decision making process for cancer treatment can impact satisfaction with care. A pooled-analysis of clinical studies from the US and Canada incorporating the Control Preferences Scale (CPS) was conducted to produce normalized data regarding patient preferences and examine differences in role preference related to country, tumor type, gender and other demographics. Methods: Patient data culled from six trials indicated the treatment decision making role preferred and the role actually experienced clinically. Fisher’s Exact Tests were performed to compare role distribution concordance and association with clinical and demographic variables. Results: Data available for 3,491 patients indicated that 25% preferred an active role, 46% a collaborative role, and 29% a passive role in their medical treatment decision making. In terms of actual experience, 30% of patients reported taking on an active role, 34% collaborative, and 36% passive. Overall, 61% of patients reported playing the role they prefer. Differences between genders in the preferred role were slight, but males achieved their preferred role more often than females (66% vs. 60%, p=0.011). More women actually took a passive role than men (40% vs. 24%, p<0.0001) as did more patients in the US than Canada (84% vs. 54%, p<0.001). Canadian patients preferred more passive than active roles (33% vs 22.4%) and US patients preferred more active to passive roles (31.9% vs 14.2%) (p<0.001). Older patients preferred a more passive role and took on that role. Differences in role preference across tumor types were negligible. Conclusions: Roughly one half of the cancer patients studied indicated that they preferred to have a collaborative relationship with physicians, the remaining patients split equally between an active and passive role. The US cohort seemed to want to be more assertive than their Canadian counterparts and women ended up playing a more passive role than they preferred. Given these gender and cross-county differences these findings highlight the need for individualized patient communication styles to be incorporated into treatment plans 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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".