Development of surgical decision⁃making aids for breast cancer patients
Bibliographic record
Abstract
ObjectiveTo develop surgical decision⁃making aids for breast cancer patients，in order to improve patient participation in decision⁃making experience and promote decision⁃making quality.MethodsGuided by the Ottawa Decision Support Framework（ODSF），based on the results of the status quo survey and qualitative interviews of patients with breast cancer participating in surgical decision⁃making，the first edition of surgical decision⁃making aids for breast cancer patients was formed through literature analysis and group discussion.The revised version of the tool was formed by the expert correspondence method.After user assessment and debugging of acceptance of the tool by patients and their families，the final version of surgical decision⁃making aids for breast cancer patients was formed.ResultsTwo rounds of expert consultation were conducted using Delphi method.The positive coefficients of the two rounds of expert consultation were 93.75%，100.00%，respectively，the expert authority coefficients were both 0.828.And the Kendall's W coefficients were 0.292，0.228，respectively，which were statistically significant（P<0.05）.The tool acceptance test showed that the tool had good acceptability and practicability.The final revision of surgical decision⁃making aids for breast cancer patients included 3 first⁃level indicators，8 second⁃level indicators，and 34 third⁃level indicators.ConclusionsThe surgical decision⁃making aids for breast cancer patients had good acceptability and practicality，which could effectively help patients to fully understand surgery⁃related information and assist patients in making high⁃quality decisions.
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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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".