Development of a systematic approach to pharmaceutical industry’s patient assistance programs on accessing unfunded cancer drugs
Bibliographic record
Abstract
Background New oncology drugs usually become commercially available several months before the funding decisions are made by provincial public payers. Increasingly, patient assistance programs are being set up by pharmaceutical companies in order to facilitate access of their new cancer drugs before public funding decisions are finalized. We discovered that there is a need to keep this information up to date and available in a central repository, thus we have created a centralized patient assistance chart for use by all who require information on accessing unfunded drugs in our province. Methodology The project was carried out at a publicly funded provincial cancer care organization that oversees parenteral and oral chemotherapy treatments across our province. The drug information pharmacist at this organization developed a method of scoping information on upcoming therapies by reviewing a series of recommendations made by various organizations that review oncology treatments. A standard process was developed for including information on the patient assistance chart that is available on the organizations website. Results As of May 2016, the repository contains information on 47 patient assistance programs involving 24 unfunded antineoplastic drugs for various indications. This compared to (7) when it was maintained by a single centre in 2004 and 10 when the process was first centralized in 2009. Conclusion The benefit of patient assistance program availability allows patients to access medications when provincial funding is not available. A standardized approach and methodology to evaluating information was established by our drug information pharmacist; thus allowing for a consistent approach to dissemination of information on assessing unfunded cancer drugs in our province.
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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 | 0.443 | 0.445 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.043 | 0.023 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".