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Paediatric Severe Community‐Acquired Pneumonia in India

2005· article· en· W2137296116 on OpenAlexfundno aff
Mallik Angalakuditi, V Bruce Sunderland

Bibliographic record

VenueJournal of Pharmacy Practice and Research · 2005
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsnot available
FundersHospital for Sick Children
KeywordsMedicineAntipyreticIntervention (counseling)AntibioticsPneumoniaCohortDosingCommunity-acquired pneumoniaPopulationInternal medicineAnalgesicAnesthesiaNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.467
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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