Bibliographic record
Abstract
SENIORS IN THE UNITED STATES are boarding busesto Canada to buy prescription drugs.'Faced with expenditures for medications that place an increasing demand on their limited income, many elderly persons are getting tickets on rented buses to visit Canadian physicians and pharmacies. 2 They can save as much as ninety percent on needed medications, with a busload of fifty seniors saving as much as $48,000 a year.3 This strange trend is likely to continue as the elderly in the United States search for a way to pay for their prescription drug expenses, not covered by Medicare, the government health insurance program for the aged. THE NEED FOR PRESCRIPTION DRUG COVERAGE AMONG MEDICARE BENEFICIARIESAs the federal health insurance program for the elderly, Medicare provides coverage for inpatient medical services under Medicare Part A. 10 It also covers many outpatient medical services with voluntary enrollment under Medicare Part B, in which most Medicare recipients opt to enroll.11 Medicare does not cover outpatient prescription drugs as a part of the covered mandated benefits package. A. The Rise of Prescription Drug UseWhen Medicare was enacted in 1965, prescription drug coverage was not a common component of health insurance.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.116 | 0.033 |
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".