Efficacy‐effectiveness gaps in oncology: Looking beyond survival
Bibliographic record
Abstract
The efficacy-effectiveness (EE) gap describes the differences in survival seen in clinical trials and routine clinical practice, where patients in real-world practice often have inferior outcomes compared to trial populations. However, EE gaps may exist beyond survival outcomes, including gaps in quality of life, toxicity, cost-effectiveness, and patient time, and these EE gaps should also influence patient and clinician treatment decisions. Failure to clearly acknowledge these EE gaps may cause patients, clinicians, and health care systems to have unrealistic expectations of the benefits of therapy across a range of important clinical and economic domains. In this commentary, the authors review the evidence supporting the existence of EE gaps in quality of life, time toxicity, cost and toxicities, and urge for further research into this important topic.
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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 | 0.048 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".