Considering and communicating uncertainty in health technology assessment
Bibliographic record
Abstract
Abstract As health technology assessment (HTA) seeks to combine complex sets of evidence, values, and perspectives to support open, accountable, and transparent decision making, uncertainty is inherent. Uncertainty is present in the clinical and economic inputs that inform HTA and is a critical factor during context-specific deliberations where the evidence is weighed and decisions are made, taking uncertainty into account through either financial or evidence-generation mechanisms. The presence and impact of uncertainty must also be communicated to all relevant stakeholders during the HTA output stage. This article summarizes the 2021 HTAi Global Policy Forum discussion on “Considering and Communicating Uncertainty in HTA” that debated some of the key challenges and opportunities regarding uncertainty in HTA. Through a combination of small and large group discussions, core themes related to the topic of uncertainty in HTA were identified. These discussions revealed that: utilization of a life cycle/HTA management approach helps manage uncertainty; genuine stakeholder input and engagement (and not just consultation) can clarify uncertainty; tolerance of risk, the relationship of risk to uncertainty, and the context in which uncertainty is considered is critical; transparent and early dialogues could be increased to further reduce the uncertainty during HTA; and communicating uncertainty in HTA outputs is critical. The paper ends with suggested next steps that HTA agencies and stakeholders (such as industry, patients, regulators, payers, and others) might take to move the field forward. The paper promotes further discussion on aspects of uncertainty that should be more openly discussed, debated, and addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.242 | 0.271 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.042 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".