Exploration of the visibility of patient input in final recommendation documentation for three health technology assessment bodies
Bibliographic record
Abstract
Health technology assessment (HTA) recommendations informed by patient concerns are seen to ensure democracy and legitimacy. We explored how written and oral patient involvement in two HTAs was reported on in publicly available final recommendations and discussion summaries of appraisal committees from three HTA bodies. We aimed to gain insights into how patient input was utilized by appraisal committees to better understand the goals of patient involvement and how these are being achieved. In each of the three HTA bodies, templated submission questionnaires provide a formal process for seeking written patient group input. Additionally, the National Institute for Health and Care Excellence (NICE) selects patient experts to provide a templated submission and attend appraisal committee meetings. For Scottish Medicines Consortium (SMC), a patient advocate and clinician combined meeting (PACE) discussed the cancer drug, referred to in the final recommendation. The discussion summaries of all appraisal committees contained references to patient involvement. Where two mechanisms for patient involvement were provided, oral input from the expert patients and PACE were more clearly documented than information from written patient group submissions. NICE reports focused on the perspective of the patient expert. The SMC report highlighted feedback from the PACE throughout. We suggest that the lack of clear reporting on the use of patient group input in deliberations and therefore accountability to patient groups limits progress in patient involvement in HTA. Patient groups may therefore not have a clear understanding of what information they can best provide to inform deliberations, and in reporting back to members.
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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 | 0.533 | 0.726 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.034 | 0.021 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".