Willingness to pay for new hormone-refractory prostate cancer (HRPC) therapies: Experience from Ontario hospitals
Bibliographic record
Abstract
6559 Background: The New Drug Funding Program (NDFP) is an IV-based government funded provincial formulary that reimburses over 90 Ontario hospitals for new and expensive anti-cancer and supportive care drugs. An expert committee considers clinical and economic evidence, including a hierarchy of benefits and evidence to inform decisions. There can be extended unpredictable delays from the time evidence is available to funding, during which hospitals decide whether to pay for therapies. This report assesses hospitals’ willingness to pay for HRPC therapies prior to a funding decision. Methods: Patterns of uptake for docetaxel (D) and zoledronic acid (Z) were analyzed from the NDFP database and compared to publication dates, hierarchy of benefits and evidence, and costs for these new HRPC therapies. Results: Analysis of NDFP data for patterns of uptake revealed an apparent cost shift from hospitals to the NDFP for D but not for Z once funding was in place. This cost shift demonstrated that despite a higher cost, shorter time from publication to funding and equivalent levels of evidence that hospitals paid for D but not for Z. The only factor that favored payment for D over Z was that D ranked higher than Z in the NDFP hierarchy of benefits: Survival was the primary outcome for D versus prevention of skeletal related events (SRE) for Z. Publication of provincial practice guidelines supporting these therapies occurred after NDFP funding was in place and would not have impacted hospitals’ decisions to pay. Despite immediate uptake of Z once NDFP funding was introduced, hospitals did not absorb the cost when funding was temporarily suspended for a 5 month period. Conclusions: Survival benefit appeared to be the primary factor impacting hospitals’ willingness to pay for unfunded HRPC therapies. This observation is consistent with the ranking of clinical benefits used to inform NDFP formulary decisions. The higher cost of D did not appear to impact hospitals’ willingness to pay. No significant financial relationships to disclose. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".