Protocol for Management of Imported Pediatric Malaria Decreases Time to Medication Administration
Bibliographic record
Abstract
BACKGROUND: A malaria management protocol was developed and implemented at a tertiary care children's hospital in September 1999. We retrospectively evaluated children admitted with malaria 10-years preimplementation and 7-years postimplementation to determine the impact the protocol had on management and time delay to appropriate antimalarial therapy. METHODS: This before and after study compared all admissions with the discharge diagnosis of malaria in the study period. Retrospective chart review was used to determine the time from emergency department (ED) registration to administration of antimalarial treatment. Other outcomes measured included mortality, length of hospital stay, and intensive care unit admission. RESULTS: Fifty-eight admissions were identified during the defined period, most of which were due to Plasmodium falciparum[r] malaria. Thirty-one (53.4%) cases were before implementation of the protocol. Children were more likely to receive appropriate investigations to assess for possible severe malaria before transfer from the ED to the ward after protocol implementation (18% vs. 63%, P = 0.005). Analysis of index cases of malaria, excluding patients diagnosed after the diagnosis of a sibling, showed there was a significant reduction in time to medication administration (8 vs. 5.5 hours, P = 0.036). CONCLUSION: After broad-based implementation of a malaria treatment protocol in a pediatric hospital, children received more thorough investigations, were more likely to receive therapy before leaving the ED and had a shorter delay before receiving appropriate antimalarial therapy.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".