The impact of practice guidelines and funding policies on the use of new drugs in advanced non‐small cell lung cancer*
Bibliographic record
Abstract
BACKGROUND: Cancer Care Ontario's (CCO) Program in Evidence-based Care has provided a credible basis for policy development and the funding of new and expensive anticancer drugs in the province of Ontario. In November 1997, vinorelbine was approved for the first-line treatment of advanced non-small cell lung cancer (NSCLC) on the basis of evidence-based practice guidelines generated by the Provincial Lung Disease Site Group. In June 1998, gemcitabine was approved as an alternative to vinorelbine for use in selected patients (e.g. significant venous access problems, peripheral neuropathy, severe toxicity to vinorelbine). A provincial drug database was used to determine the impact that these new policies had on the rate of vinorelbine and gemcitabine uptake within the CCO new drug funding programme. METHODS: Drug utilization data for vinorelbine and gemcitabine from October 1997 to June 1999 were obtained from the CCO drug database. Individual patient data consisted of age, gender, first-line agent used, number of treatments, duration of therapy, treatment location (regional cancer centre vs. other) and total cost. Demographic and drug utilization data were analysed descriptively as means, medians, or proportions. Multivariable logistic regression analysis was then used to identify factors associated with the selection of gemcitabine over vinorelbine, as a first-line therapy. RESULTS: Following the approval of the first policy in November 1997, there was a rapid adoption of vinorelbine use in new NSCLC patients. When the gemcitabine policy was approved in June 1998, there was a rapid uptake in its use reaching a stable plateau of approximately 15% of all NSCLC patients within 9 months. The logistic regression analysis identified patient age greater than 65 years [odds ratio (OR) = 1.90, P = 0.001] and treatment in a non-regional cancer setting (OR = 1.71, P = 0.008) as significant predictors of gemcitabine utilization. Overall, the mean drug cost per patient treated with first-line gemcitabine was significantly higher than vinorelbine (Can 2590 dollars vs. Can 1030 dollars, P < 0.001). CONCLUSIONS: The new funding policies were associated with a rapid increase in drug utilization reaching a stable plateau within 9 months. Factors contributing to the usage of these new drugs for NSCLC included patient characteristics, such as age and treatment location.
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 | 0.050 | 0.271 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".