OD09 How Are Population/Intervention/Comparator/Outcomes Criteria For Pharmaceutical Assessments Determined Around The World?
Bibliographic record
Abstract
Introduction Defining the population, intervention, comparator, outcomes (PICO) criteria is an essential step prior to performing a health technology assessment (HTA), but variations exist in how this step is performed. Methods A scoping review was performed to compare the processes and guidance provided for developing the PICO criteria for the assessment of new medicines across Australia, the UK, Canada, the US, European Union (as a single jurisdiction), Germany, France, the Netherlands, South Korea, and Taiwan. The websites of HTA agencies in these jurisdictions were searched for methodological guidance, and PubMed, Embase, and the HTA database were also searched for published literature on the topic of the process or methods for developing the PICO criteria. Results Two main approaches are used for developing the PICO criteria. In the UK, US, and European Union, a separate scoping process is used; in the remaining countries, the pharmaceutical manufacturer defines the PICO criteria as part of developing their dossier for submission. Guidance on PICO elements were similar in content but highly varied in the degree of guidance provided. The largest differences were in whether outcomes for people beyond the treated individual were recommended to be assessed. Conclusions A separate scoping phase allows stakeholder input into the criteria, which is important with the shift to incorporating more patient input into each phase of HTA. It can come at the cost of timeliness, so requires manufacturers to engage with the HTA systems earlier in the process.
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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 | 0.214 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".