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 insurers are struggling with funding decisions regarding increasingly expensive new cancer drugs. Increasingly, oncologists are contributing 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" framework informed the design of a six-module multistakeholder decision process blending evidence-based traditional technology assessment methods with individual and cultural values elicitation. The tool was piloted in three settings: (1) videotaped simulated multistakeholder deliberation sessions; (2) clinical oncology leaders; and (3) a regional (Canadian provincial) pharmacy and therapeutics committee making formulary decisions. The modules involve: decision clarification, drug eligibility screening (filtering), clinical performance scoring index, cost modeling, data integration and values clarification, and process evaluation. RESULTS: The tool was feasible to use, acceptable to participants, 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 requested its incorporation into organization-based decision making. CONCLUSION: The decision tool can facilitate priority-based cancer drug funding decisions that meet the conditions of fairness as perceived by participants, including oncologists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".