Bibliographic record
Abstract
14 Background: As medication prices rise, the sustainability of health care systems has been increasingly questioned. Health technology assessment (HTA) could be employed to maximize value when budgets are limited. Methods: The pan-Canadian Oncology Drug Review (pCODR) is an evidence-based, cancer drug review process that guides formulary decision-making. As of 10/30/16, data from all 93 reviews and economic guidances were abstracted for price of medication, total health care cost per patient, cost-utility provided by the submitter, and the re-analysis by pCODR. Regression analysis was employed to identify correlations. Expected use of therapy was estimated employing mortality data from the Canadian Cancer Society. An optimal formulary was then developed, with value for money as the primary concern. Results: Of the 93 reviews, 11 were not finalized, 3 were withdrawn, and 1 was suspended. 4 reviews were excluded since the base-case was ambiguous. Of included reviews, 13% were recommended for funding, 66% were recommended conditional on improved cost-effectiveness, and 22% were rejected. The median drug price per 28-day cycle was $7,567 (range $2,800-$18,435), with no annual difference from 2012-2016 (p=0.49). The median best-estimate of cost-utility was $188,537/QALY (IQR $127,399/QALY) with a median net increase in health system cost of $66,069/patient (IQR $90,466). The median difference between pCODR’s best estimate and the submitter’s was $61,240/QALY (IQR $73,656/QALY). The submitter’s estimate of cost effectiveness was correlated with pCODRs assessment (R² = 0.65, p<0.01). Cost per 28-day cycle was a weak predictor of value (R² = 0.01, p<0.01), and not of health system cost (R² = 0.17, p=0.11). In the Canadian context, funding all efficacious medications would require a total of $5.91 billion producing 31,705 QALYs, annually. Funding the system with $1.12 billion for new medicines by first-come-first-served principle, yields 5,966 QALYs over 16 drugs, annually. By prioritizing based on value, $1 billion allows for funding of 22 drugs producing 9,665 QALYs, annually. $2 billion would increase annual QALYs to 15,792 over 26 drugs. Conclusions: Price of medications should not be used as a heuristic for value. An optimized formulary requires practical deployment of HTA.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 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; 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".