Bibliographic record
Abstract
What Is the Issue? Drug shortages are a global issue with complex dynamics. Shortages can occur because of disruption at any point along the drug supply chain. Several strategies are used in Canada to prevent or alleviate the effects of drug shortages, including mandatory reporting by drug manufacturers. An understanding of the amount and types of real or potential harms caused to patients can inform policy decisions around drug shortage management and prevention. What Did We Do? We searched for literature providing evidence on patient outcomes associated with supply chain disruptions of pharmaceuticals and vaccines. An information specialist conducted a search of peer-reviewed literature sources published between January 1, 2003, and September 13, 2023. Documents were excluded if the objective was to investigate the potential effects of a drug shortage in the absence of an actual drug shortage or if the outcomes were not direct patient harms. What Did We Find? One scoping review and 33 nonrandomized studies were identified that evaluated patient outcomes associated with supply chain disruptions of pharmaceuticals and vaccines. We identified a wide variety of drug classes experiencing shortages. The most frequently reported shortages were anesthetics, oncology drugs, vaccines, drugs for the treatment of COVID-19, antimicrobials, and small-volume parenteral solutions. Most of the included primary studies concluded that the replacement drug or protocol was a safe or acceptable alternative to the shortage drug. The subset of primary studies that concluded that the replacement drug or protocol was not a safe or acceptable alternative to the shortage drug reported worse outcomes in health system use (including length of hospital stay), adverse events, disease progression, and mortality. What Does This Mean? Drug shortages have the potential to cause harm to patients and some drug shortages may have a greater impact on patients than others. The ability to predict which drugs could cause the greatest harm during a supply disruption would be a great benefit for future planning. The diversity of drugs experiencing shortages and their associated harms emphasizes that decision-makers may need to take a case-by-case approach when developing policies meant to lessen the impact of drug shortages.
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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 | 0.046 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| 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".