Estimation of Drug Cost Avoidance and Pathology Cost Avoidance through Participation in NCIC Clinical Trials Group Phase III Clinical Trials in Canada
Bibliographic record
Abstract
BACKGROUND: Cost avoidance occurs when, because of provision of a drug therapy [drug cost avoidance (dca)] or a pathology test [pathology cost avoidance (pca)] during trial participation, health care payers need not pay for standard treatments or testing. The aim of our study was to estimate the total dca and pca for Canadian patients enrolled in relevant phase iii trials conducted by the ncic Clinical Trials Group. METHODS: Phase iii trials that had completed accrual and resulted in dca or pca were identified. The pca was calculated based on the number of patients screened and the test cost. The dca was estimated based on patients randomized, the protocol dosing regimen, drug cost, median dose intensity, and median duration of therapy. Costs are presented in Canadian dollars. No adjustment was made for inflation. RESULTS: From 1999 to 2011, 4 trials (1479 patients) resulted in pca and 17 trials (3195 patients) resulted in dca. The total pca was estimated at $4,194,849, which included testing for KRAS ($141,058), microsatellite instability ($18,600), and 21-gene recurrence score ($4,035,191). The total dca was estimated at $27,952,512, of which targeted therapy constituted 43% (five trials). The combined pca and dca was $32,147,361. CONCLUSIONS: Over the study period, trials conducted by the ncic Clinical Trials Group resulted in total cost avoidance (pca and dca) of approximately $7,518 per patient. Although not all trials lead to cost avoidance, such savings should be taken account when the financial impact of conducting clinical research is being considered.
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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 | 0.031 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| 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) | 0.002 | 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".