Alignment of Practice Guidelines with Targeted-therapy Drug Funding Policies in Ontario
Bibliographic record
Abstract
BACKGROUND: We evaluated clinical practice guideline (cpg) recommendations from Cancer Care Ontario's Program in Evidence-Based Care (pebc) for molecularly targeted systemic treatments (tts) and subsequent funding decisions from the Ontario Ministry of Health and Long-Term Care. METHODS: We identified pebc cpgs on tt published before June 1, 2010, and extracted information regarding the key evidence cited in support of cpg recommendations and the effect size associated with each tt. Those variables were compared with mohltc funding decisions as of June 2011. RESULTS: From 23 guidelines related to 17 tts, we identified 43 recommendations, among which 38 (88%) endorsed tt use. Among all the recommendations, 38 (88%) were based on published key evidence, with 82% (31 of 38) being supported by meta-analyses or phase iii trials. For the 38 recommendations endorsing tts, funding was approved in 28 (74%; odds ratio related to cpg recommendation: 29.9; p = 0.003). We were unable to demonstrate that recommendations associated with statistically significant improvements in overall survival [os: 14 of 16 (88%) vs. 8 of 14 (57%); p = 0.10] or disease- (dfs) or progression-free survival [pfs: 16 of 21 (76%) vs. 3 of 5 (60%); p = 0.59] were more likely to be funded than those with no significant difference. Moreover, we did not observe significant associations between funding approvals and absolute improvements of 3 months or more in os [6 of 6 (100%) vs. 3 of 6 (50%), p = 0.18] or pfs [6 of 8 (75%) vs. 10 of 12 (83%), p = 1.00]. CONCLUSIONS: For use of tts, most recommendations in pebc cpgs are based on meta-analyses or phase iii data, and funding decisions were strongly associated with those recommendations. Our data suggest a trend toward increased rates of funding for therapies with statistically significant improvements in os.
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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 | 0.094 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".