Evolving Recommendations for Patient Populations Among Oncology Medicines: A Quantitative and Qualitative Analysis
Bibliographic record
Abstract
After a medicine has been tested in pivotal trials, regulators, health technology assessment (HTA) organizations, and professional societies make decisions about the patients best served by the medicine. This study assesses how the patient populations for oncology medicines (2010-2023) are defined (1) at trial, (2) regulatory submission, (3) upon approval for marketing authorization, (4) at submission, and (5) recommendation by the HTA, and (6) in clinical guidelines in Australia, Canada, the Netherlands, the United Kingdom, and the United States. Based on 25 populations for oncology medicines, we developed a framework for describing oncology populations consisting of 20 elements in four domains: disease specifications, patient characteristics, treatment position, and exclusion criteria. In exploratory analyses, we tabulated any observed variation in these framework elements throughout the six steps in the lifecycle of a medicine. On average, 10 (95% confidence interval [CI]: 9.2-10.9) potential adjustments were made, 2.3 (95% CI: 2.0-2.5) by each decision-maker. The adjustments by pharmaceutical developers focused mostly on the disease specifications (0.5 of the average 0.8 adjustments, 63%), while adjustments by regulators, HTA organizations, and guideline developers predominantly targeted the treatment's position (range: 0.5/1.3 [36%] in guidelines to 0.6/1.0 [58%] in regulatory approvals). Each decision-maker on average modifies 1.0 element (out of 2.3 [43%]) that was previously adjusted by another decision-maker. The multiple differences observed in the description of patient populations reflect inconsistency in reporting between decision-makers, complicating communication to patients and potentially affecting access to medicines. The developed framework can support consistent reporting across stakeholders and countries.
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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 | 0.075 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".