Managing drug shortages during a pandemic: tocilizumab and COVID-19
Bibliographic record
Abstract
he COVID-19 pandemic has revealed weaknesses in global manufacturing and distribution of medications, exacerbating many pre-existing limitations and inequities in drug supply and creating new shortages. 1-3 Supply chains have been disrupted 4 as many were designed for "just-in-time" management of drug inventory to reduce the costs of storage and reduce the risk of drug expiration. The problem of mismatched supply and demand can be exacerbated by people and institutions hoarding drugs in times of supply uncertainty. he emergence of SARS-CoV-2 prompted testing of many newly developed or existing repurposed therapies as treatments for COVID-19. Expecting drug manufacturers to increase the supply of all candidate therapies or health care providers to stockpile inventory before drugs are proven effective would be unreasonable. Thus, when a new drug is shown to be effective, there will likely be at least a temporary shortage of supply unless it is already widely available. Medications are at greatest risk for prolonged shortage when their demand surges unexpectedly and manufacturing and distribution are not diversified. Tocilizumab is an interleukin-6 receptor antagonist that has recently been found to reduce mortality in patients hospitalized for As the number of patients admitted to hospital with COVID-19 in Canada increases, demand for tocilizumab is on the rise and supply is likely inadequate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".