Prescription Drugs: Comparison of DOD and VA Direct Purchase Prices
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "When GAO compared prices paid by the Department of Defense (DOD) and the Department of Veterans Affairs (VA) for a sample of 83 drugs purchased in the first calendar quarter of 2012, DOD's average unit price for the entire sample was 31.8 percent ($0.11 per unit) higher than VA's average price, and DOD's average unit price for the subset of 40 generic drugs was 66.6 percent ($0.04 per unit) higher than VA's average price. However, VA's average unit price for the subset of 43 brand-name drugs was 136.9 percent ($1.01 per unit) higher than DOD's average price. These results were consistent with each agency obtaining better prices on the type of drugs that made up the majority of its utilization: generic drugs accounted for 83 percent of VA's utilization of the sample drugs and brand-name drugs accounted for 54 percent of DOD's utilization of the sample drugs. DOD officials told GAO that in certain circumstances they are able to obtain competitive prices for brand-name drugs--even below the prices for generic equivalents--and therefore will often preferentially purchase brand-name drugs."
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".