The undisclosed disclosures: The dollar-outcome relationship in resuscitative endovascular balloon occlusion of the aorta
Bibliographic record
Abstract
BACKGROUND: Despite its rapid evolution, resuscitative endovascular balloon occlusion of the aorta (REBOA) remains a controversial intervention that continues to generate active research. Proper conflict of interest (COI) disclosure helps to ensure that research is conducted objectively, without bias. We aimed to identify the accuracy of COI disclosures in REBOA research. METHODS: Literature search was performed using the keyword "REBOA" on PubMed. Studies on REBOA with at least one American author published between 2017 and 2022 were identified. The Centers for Medicare and Medicaid Services Open Payments database was used to extract information regarding payments to the authors from the industry. This was compared with the COI section reported in the manuscripts. Conflict of interest disclosure was defined as inaccurate if the authors failed to disclose any amount of money received from the industry. Descriptive statistics were performed. RESULTS: We reviewed a total of 524 articles, of which 288 articles met the inclusion criteria. At least one author received payments in 57% (165) of the articles. Overall, 59 authors had a history of payment from the industry. Conflict of interest disclosure was inaccurate in 88% (145) of the articles where the authors received payment. CONCLUSION: Conflict of interest reports are highly inaccurate in REBOA studies. There needs to be standardization of reporting of conflicts of interest to avoid potential bias. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.
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 | 0.059 | 0.427 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".