Multiphase evaluation of portable medicines quality screening devices
Bibliographic record
Abstract
Substandard and falsified (SF) medicines have important but neglected consequences including increased morbidity and mortality, economic losses, and diminished public confidence in health systems. SF antimicrobials, particularly those containing reduced quantities of active pharmaceutical ingredients (APIs), may also be key but overlooked drivers of antimicrobial resistance Substandard medicines result from negligence and errors made during the manufacturing process by authorized manufacturers or degradation in supply chains. Falsified medicines are the result of criminal activity. Falsified medicines purport to be real, authorized medicines but are deliberately and fraudulently mislabeled with respect to their identity and/ or source Falsified medicines usually have packaging that are copies of a genuine product and may contain the APIs, although often at the incorrect amount, or, more commonly, they contain other API(s) or none at all. The term "falsified medicines," adopted by the World Health Assembly in May 2017, references the public health issues of poor quality medicines rather than the term "counterfeit" that refers to trademark infringement. As countermeasures vary according to the type of "defect," understanding the differences between the types of poor quality medicines is essential from a public health and regulatory perspective.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".