Practical issues in assisting shared decision‐making
Bibliographic record
Abstract
To facilitate treatment decision-making, one aims to provide information, present it in a way that makes it as easy as possible to understand, and to help the decision-maker through the cognitive processes that result in a treatment decision. Decision aids aim to accomplish just these goals and this paper identifies practical issues that we have encountered in creating a decision aid for men with early stage prostate cancer. We highlight the results of studies we carried out to provide an empirical basis for the decision aid that we were developing. Several of the studies were designed to identify what information key players (health professionals, patients and family members) thought was important for the decision-making process. Another investigation studied methodological considerations in identifying important information. The final study focused on presentation issues. These studies, designed to explore what information was considered important, found great variability among both health care professionals involved in treating patients with prostate cancer (urologists, radiation oncologists, nurses in cancer clinics, and radiation technologists) and among the patients, themselves. The studies also showed that not all information contained within a typical category is of equal importance. A methodological study showed that the information that patients deem to be important to their decision depends on whether they are rating the information that could be provided, or questions that could be answered. Finally, presentation studies showed that the various formats used in presenting quantitative information are processed with differing degrees of accuracy and ease. Each of the above results has implications for those creating decision aids; these implications are highlighted.
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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 | 0.211 | 0.379 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".