Reevaluation of Emergency Drug Management in a Tertiary Care Mother-Child Hospital
Bibliographic record
Abstract
Purpose To evaluate the management of emergency drugs in a mother-child teaching hospital. Methods A physical inventory of all the resuscitation carts, emergency carts, and emergency boxes was taken. Fifteen compliance criteria were established to evaluate partial trays of emergency medications. The contents of full and partial emergency medication trays and boxes were revised, and an improved process was implemented based on a review of the literature. The research team included 2 pharmacists, 1 anesthetist, 1 intensivist, 1 emergency doctor, 1 nurse, and 1 research assistant. Results Before the harmonization process, there were 11 full resuscitation carts with 48 items and 30 partial emergency carts with an average item count of 15.4 ± standard deviation 4.4, as well as 16 pediatric boxes and 3 emergency boxes in pediatrics and obstetrics, respectively. During the evaluation process, 1,911 distribution units were checked, 2.5% of which had expired. Following the process there were 14 identical resuscitation carts with 43 items and 25 emergency carts with 21 items. Conclusion There are few examples of steps that can be taken to evaluate and update the management of emergency medications in health care facilities. This evaluative study outlines an approach that entailed taking a physical inventory, evaluating the process, and improving the management model within a tertiary care university hospital center. A review of the new process will be performed in 12 months' time.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".