The Economic Contribution of Industry-Sponsored Pharmaceutical Clinical Trials
Bibliographic record
Abstract
PURPOSE: In pharmaceutical clinical trials, industrial sponsors pay for study drugs and related healthcare services. We conducted a study to determine industry's economic contribution of these trials to the Alberta healthcare system. Methods: We used data from two trial centers for cancer and non-cancer trials at the University of Alberta. For each trial (cancer, non-cancer), we calculated the cost of drugs provided by the sponsors using the market price, the cost of clinical services, and the cost of administrative services that they paid. We extrapolated these results to all trials in Alberta based on information obtained from the registration website ClinicalTrials.gov. Results: Our sample consisted of 40 non-cancer and 39 cancer drug trials which were initiated in 2012. The monetary value of the industry sponsors' contribution was $799,055 per non-cancer and $630,243 per cancer drug trial. Drugs (in-trial and post-trial) accounted for 84% of the total contribution of the non-cancer drug trials whereas it represented 93% of all trial-related contributions in the cancer category. The total province-wide contribution of industry-sponsored drug trials which were initiated in 2012 was estimated to be $101 million, including open-label drugs in the non-cancer category. Conclusions: Industry-sponsored pharmaceutical trials represent a major economic contributor to clinical research within the 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 | 0.062 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".