User-Managed Inventory: An Approach to Forward-Deployment of Urgently Needed Medical Countermeasures for Mass-Casualty and Terrorism Incidents
Bibliographic record
Abstract
The user-managed inventory (UMI) is an emerging idea for enhancing the current distribution and maintenance system for emergency medical countermeasures (MCMs). It increases current capabilities for the dispensing and distribution of MCMs and enhances local/regional preparedness and resilience. In the UMI, critical MCMs, especially those in routine medical use ("dual utility") and those that must be administered soon after an incident before outside supplies can arrive, are stored at multiple medical facilities (including medical supply or distribution networks) across the United States. The medical facilities store a sufficient cache to meet part of the surge needs but not so much that the resources expire before they would be used in the normal course of business. In an emergency, these extra supplies can be used locally to treat casualties, including evacuees from incidents in other localities. This system, which is at the interface of local/regional and federal response, provides response capacity before the arrival of supplies from the Strategic National Stockpile (SNS) and thus enhances the local/regional medical responders' ability to provide life-saving MCMs that otherwise would be delayed. The UMI can be more cost-effective than stockpiling by avoiding costs due to drug expiration, disposal of expired stockpiled supplies, and repurchase for replacement.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".