Bibliographic record
Abstract
Provide information on drug importation programs, including (1) a brief description of the federal drug importation law, (2) summaries of other state importation laws, (3) a list of states that recently proposed drug importation legislation, and (4) a summary of sHB 7267 (2019), which would have established such a program in Connecticut. SummaryFederal law allows the importation of drugs from Canada only if it poses no additional public health and safety risk and results in significantly reduced costs to the American consumer (21 U.S.C. § 384).Generally, drug importation (or re-importation) programs allow state agencies, pharmacists, and wholesalers to import drugs from Canada for sale or distribution to state residents.Federal law, among other things, allows importation programs if they are approved by the federal Department of Health and Human Services (HHS).To date, HHS has not approved any state prescription drug importation program, although the president has directed the HHS secretary to work with Florida towards program approval.We were able to identify at least four states ( Colorado (2019), Florida (2019), Maine (2019) and Vermont (2018)) that have recently passed enabling legislation allowing the applicable state agency to begin establishing a program and seek HHS approval.In general, each state law requires at least the federally required minimum supply chain documentation.Supply chain documentation, also known as "track-and-trace," requires importers to know the physical location of the drug at all times, as well as information about how long it spent at each location, ownership records, packaging configurations, environmental storage conditions, and other information pertinent to
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.551 | 0.326 |
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".