6-STEPPPs: A Modular Tool to Facilitate Clinician Participation in Fair Decisions for Funding New Cancer Drugs
Bibliographic record
Abstract
Purpose: To design a tool to assist clinician participation with cancer drug funding decisions. Public policy-makers and insur-ers are struggling with funding decisions regarding increasingly expensive new cancer drugs. Increasingly, oncologists are con-tributing to the process of review that leads to such decisions. We were asked to design a system for ranking new cancer drugs for priority-based funding decisions. Methods: The “Accountability for Reasonableness ” frame-work informed the design of a six-module multistakeholder de-cision process blending evidence-based traditional technology assessment methods with individual and cultural values elicita-tion. The tool was piloted in three settings: (1) videotaped simu-lated multistakeholder deliberation sessions; (2) clinical oncology leaders; and (3) a regional (Canadian provincial) pharmacy and therapeutics committee making formulary decisions. The mod-ules involve: decision clarification, drug eligibility screening (filter-ing), clinical performance scoring index, cost modeling, data integration and values clarification, and process evaluation. Results: The tool was feasible to use, acceptable to partici-pants, and able to rank candidate drugs. The pharmacy and therapeutics committee with whom it was tested used the tool as a part of their deliberations, and the tumor group leaders re-quested its incorporation into organization-based decision making. Conclusion: The decision tool can facilitate priority-based cancer drug funding decisions that meet the conditions of fair-ness as perceived by participants, including oncologists.
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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 | 0.042 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".