Financial conflicts of interest of clinicians making submissions to the pan-Canadian Oncology Drug Review: a descriptive study
Bibliographic record
Abstract
OBJECTIVES: This study examines financial conflict of interest (FCOI) of clinicians who made submissions to the pan-Canadian Oncology Drug Review (pCODR), the arm of the Canadian Agency for Drugs and Technology in Health that recommends whether oncology drug indications should be publicly funded. Final reports from pCODR published between October 2016 and February 2019 were examined. DESIGN: Descriptive study. DATA SOURCES: Website of pCODR. INTERVENTIONS: None. PRIMARY AND SECONDARY OUTCOMES: The primary outcome is the number of submissions declaring FCOI. Secondary outcomes are the number of times where clinicians agreed and disagreed with preliminary recommendation from pCODR and the association between the distribution of individual clinicians' FCOI and pCODR's funding recommendations. RESULTS: There were 46 drug indication reports from pCODR. Clinicians made 261 submissions. Clinicians declared they received payments from companies 323 times and named 38 different companies making those payments a total of 500 times. Financial conflicts with drug companies were declared in 176 (66.3%) of all submissions. In 21 (45.7%) of the 46 drug indications, 50% or more of the clinicians had a conflict with the company making the drug. Clinicians commented on 37 preliminary recommendations. In all 25 where pCODR recommended funding or conditional funding, the clinicians either agreed or agreed in part. pCODR recommended that the drug indication not be funded 12 times and 9 times clinicians disagreed with that recommendation. The distribution of clinician responses was statistically significantly different depending on whether pCODR recommended funding/conditional funding or do not fund p<0.0001 (Fisher exact test). The distribution of clinicians' FCOI differed depending on whether the recommendation was fund/conditional fund or do not fund p=0.027 (Fisher exact test). CONCLUSION: Financial conflicts with pharmaceutical companies are widespread among experts making submissions to the pCODR.
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 | 0.019 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".