COST-EFFECTIVENESS IMPACTS CANCER CARE FUNDING DECISIONS IN BRITISH COLUMBIA, CANADA, EVIDENCE FROM 1998 TO 2008
Bibliographic record
Abstract
OBJECTIVES: The Priorities and Evaluation Committee (PEC) funding recommendations for new cancer drugs in British Columbia, Canada have been based on both clinical and economic evidence. The British Columbia Ministry of Health makes funding decisions. We assessed the association between cost-effectiveness of cancer drugs considered from 1998 to 2008 and the subsequent funding decisions. METHODS: All proposals submitted to the PEC between 1998 and 2008 were reviewed, and the association between cost-effectiveness and funding decisions was examined by (i) using logistic regression to test the hypothesis that interventions with higher incremental cost-effectiveness ratios (ICERs) have a lower probability of receiving a positive funding decision and (ii) using parametric and nonparametric tests to determine if a statistically significant difference exists between the mean cost-effectiveness of funded versus not funded proposals. A sub-analysis was conducted to determine if the findings varied across different outcome measures. RESULTS: Of the 149 proposals reviewed, 78 reported cost-effectiveness using various outcome measures. In the proposals that used life-years gained as the outcome (n = 22), a statistically significant difference of nearly $115,000 was observed between the mean ICERs for funded proposals ($42,006) and for unfunded proposals ($156,967). An odds ratio indicating higher ICERs have a lower probability of being funded was also found to be statistically significant (p < .05). CONCLUSIONS: Economic evidence appears to play a role in British Columbia cancer funding decisions from 1998 to 2008; other decision-making criteria may also have an important role in recommendations and subsequent funding decisions.
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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 | 0.014 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".