Development and Assessment of a Shared Decision-making Assistive Tool for the Treatment of Pulmonary Nodules
Bibliographic record
Abstract
Background: Screening high-risk populations for lung cancer with low-dose computed tomography revealed 36.7% of them had pulmonary nodules. Only 0.4% of them were cancerous and require treatment. However, in Taiwan, there’s a lack of shared decision-making (SDM) aids to guide patients for treatment and follow-up care. Therefore, the development and assessment of the tool is important before applied it to the general public. Objective: SDM aids developed in accordance with the International Patient Decision Aid Standards (IPDAS), which are used as the content validity index (CVI) for the assessment of the assistive tool. Its accessibility was, in turn, examed by the Patient Education Materials Assessment Tool (PEMAT). Methods: A cross-sectional study was conducted in these stages as the following: (1) The Ottawa Decision Support Framework (ODSF) was used to guide the development of the decision aid that followed the development process along the lines recommended in IPDAS; (2) Selected through purposive sampling method, 9 patients with pulmonary nodules who had SDM experience were asked to participate as content validation experts to improve the accessibility and viability of the content of the decision aid. (3) Selected through purposive sampling method, 2 patients with pulmonary nodules who participated in SDM process during the project were asked to participate as content validation experts to appraise the accessibility and viability of the revised content of the decision aid. Results: The scale of CVI valued by IPDAS was 0.9. Accessibility and viability rates of the decision aid reviewed from the Chinese version of PEMAT was 96.6% and 100%, respectively, in patients experienced SDM process before; whereas in patients currently participate in SDM process were both 100%. Conclusion: The results verified the efficacy of the purported assistive tool along with its content designed for easy accessibility. This tool can be widely used among medical centers to improve the quality of care for patients with pulmonary nodules.
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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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".