Adaptive health technology assessment informing guidelines
Bibliographic record
Abstract
In the present era, global decision-makers encounter numerous challenges when it comes to allocating optimal health care to their populations, primarily due to limited resources. While technology has seen remarkable advancements, determining which technologies are useful and possess robust clinical and economic evidence is paramount for informed decision-making. Health Technology Assessment (HTA) and clinical guidelines employ health economic models to aid in this process. However, these models demand significant resources and collaborations from diverse groups of experts, bodies, and organizations. Consequently, decision-making bodies are actively exploring the adoption of adaptive HTA (AHTA) as an alternative approach. AHTA involves leveraging published decisions based on health economic modeling from other countries, allowing for more efficient utilization of available data. This article delves into the essential steps and processes employed in conducting AHTA and exemplifies its application in the oncology guidelines of the National Cancer Grid in India.
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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 | 0.096 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".