Defining the clinical improvement in cancer drug therapy: implications for priority setting in healthcare
Bibliographic record
Abstract
An explicit approach to funding decisions has become increasingly important to ensure fairness and consistency in resource allocation in cancer therapy. Funding decisions are often based on whether a treatment is 'medically necessary' and the level of clinical improvement. Currently, there is a lack of consensus on defining different levels of clinical improvement, leading to controversies on the values placed on different outcomes and degrees of clinical improvements during funding evaluation. More information on how clinicians and patients define the levels of clinical improvement can help ensure the evaluation and decision-making processes of funding to become more predictable, consistent, understandable and therefore accountable to providers and consumers of healthcare.
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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 | 0.068 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| 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; 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".