Bibliographic record
Abstract
Background: The influence of financial ties to pharmaceutical companies remains controversial.We aimed to assess a potential relationship between pharmaceutical payments and prescription patterns for degarelix and denosumab.Methods: Medicare Provider Utilization and Payment Data: Physician and Other Supplier Public Use File (Medicare B) data containing 2012 claims compared to OpenPayments (Sunshine Act) data for the second half of 2013.Urologists and medical oncologists who billed Medicare for degarelix or denosumab were cross-referenced in both databases and payments were aggregated into a consolidated dataset.Adjusted beneficiary count and total Medicare reimbursement were compared according to receipt of Sunshine payment, and an association between Sunshine payment amount and total Medicare reimbursement was also assessed.Results: Of the 160 prescribers of degarelix and 1507 prescribers of denosumab, 91 (57%) and 854 (57%) received Sunshine payment, respectively.Degarelix prescribers who received Sunshine payment had higher median total Medicare reimbursement ($13 257 vs. $9554; p=0.01).Denosumab prescribers who received Sunshine payment had both higher median adjusted beneficiary count (55 vs. 50, p & lt; 0.001) and median total Medicare reimbursement ($69 620 vs. $60 732, p & lt; 0.001).On multivariable analysis, both receipt of Sunshine payment (adjusted median difference $5844, 95% CI $937-$10 749) and oncology specialty (adjusted median difference $34 380, 95% CI $26 715-$42 045) were independently associated with total Medicare reimbursement for denosumab.Conclusions: In the case of degarelix and denosumab, there is a weak association between pharmaceutical company payments on prescribers' prescription behavior patterns.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.500 | 0.187 |
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".