Drug Shortages: A Scoring System to Evaluate and Prioritize the Clinical Importance of Drugs in Canada
Bibliographic record
Abstract
Drug shortages are an ongoing global health issue and pose significant challenges to policy-makers and health care providers. They may result from various factors (e.g., supply chain disruptions and recalls) and have a negative impact on patient outcomes and health care expenditures. The ability to accurately predict drugs at risk of a shortage would be a vital aspect in the effective management of drug shortages and help ensure proactive measures for consistent drug supply and optimized patient care; however, these drugs must also be prioritized in a meaningful way to identify high priority targets among the large number of drugs in the market and help ensure resources are allocated to the most important at-risk drugs. The purpose of this Environmental Scan aimed to identify a systematic method of objectively evaluating and ranking the value of drugs based on important clinical criteria and adapt the selected tool to prioritize the clinical importance of drugs in Canada. A total of 5 potential scoring systems were considered, and 1 scoring system was selected and adapted (based on expert panel member feedback). Finally, an assessment of 9 clinical criteria was made across 5 broad categories. The criteria include an evaluation of disease severity, size of the population, clinical guidelines, comparator interventions limitations (unmet needs), improvement of efficacy or effectiveness, improvement of safety and tolerability, improvement of patient-reported outcomes (PRO), type of medical service (e.g., symptom relief, prolonging life, cure), and public health interest. The proposed tool can be used in situations where decision-making requires an objective and systematic evaluation of drugs' clinical importance. Additionally, it will help inform a drug shortage risk framework in Canada, which will consider both the clinical importance and supply chain risks of drugs as part of the Health Canada Drug Shortages Task Force.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".