Development of an international template to support patient submissions in Health Technology Assessments
Bibliographic record
Abstract
OBJECTIVES: To develop an international template to support patient submissions in Health Technology Assessments (HTAs). This was to be based on the experience and feedback from the implementation and use of the Scottish Medicines Consortium's (SMC) Summary Information for Patient Groups (SIP). METHODS: To gather feedback on the SMC experience, web-based surveys were conducted with pharmaceutical companies and patient groups familiar with the SMC SIP. Semistructured interviews with representatives from HTA bodies were undertaken, along with patient group discussions with those less familiar with the SIP, to explore issues around the approach. These qualitative data informed the development of an international SIP template. RESULTS: Survey data indicated that 82 percent (18 of 22 respondents) of pharmaceutical company representatives felt that the SIP was worthwhile; 88 percent (15/17) of patient group respondents found the SIP helpful. Both groups highlighted the need for additional support and guidance around plain language summaries. Further suggestions included provision of a glossary of terms and cost-effectiveness information. Patient group interviews supported the survey findings and led to the development of a new template. HTA bodies raised potential challenges around buy-in, timing, and bias connected to the SIP approach. CONCLUSIONS: The international SIP template is another approach to support deliberative processes in HTA. Although challenges remain around writing summaries for lay audiences, along with feasibility considerations for HTA bodies, the SIP approach should support more meaningful patient involvement in HTAs.
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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 | 0.191 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".