Paediatric Severe Community‐Acquired Pneumonia in India
Bibliographic record
Abstract
ABSTRACT Aim To evaluate the prescribing patterns of antibiotics and antipyretics and their dosage for severe community‐acquired pneumonia (SCAP) in a paediatric population before and after an educational intervention in a rural Indian hospital. Method Physician prescribing patterns for SCAP were collected prospectively in a cohort of paediatric patients (pre‐intervention group). An educational intervention strategy was developed and implemented using data from baseline prescribing patterns and the Therapeutic Guidelines: Antibiotic recommendations for treatment of SCAP. An analysis to evaluate the impact of drug selection and dosing following the intervention was conducted in a second patient cohort (post‐intervention group). Results There were 146 patients in the pre‐intervention group and 155 in the post‐intervention group. Antibiotic choices were within the guidelines in both groups. All of the patients in the pre‐intervention group received dexamethasone with every dose of antibiotic. Post‐intervention, the use of dexamethasone was eliminated. For the pre‐intervention group, 97% of the antibiotic doses and 55% of the antipyretic doses were classified as inappropriate. Inappropriate prescribing of antibiotic and antipyretic doses was significantly reduced to 89% (p = 0.002) and 10% (p < 0.001) respectively, following the intervention. Conclusion There was some improvement in prescribing appropriate doses of antibiotics and antipyretics for SCAP following the educational intervention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".