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Appropriate antibiotic utilization in seniors prior to hospitalization for community-acquired pneumonia is associated with decreased in-hospital mortality

2004· article· en· W2016459812 on OpenAlexaffabout
David H. Johnson, Keumhee C. Carrière, Thomas J. Marrie

Bibliographic record

VenueJournal of Clinical Pharmacy and Therapeutics · 2004
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of SaskatchewanAlberta HealthUniversity of Alberta
Fundersnot available
KeywordsMedicineMedical prescriptionAntibioticsPneumoniaOdds ratioCommunity-acquired pneumoniaEmergency medicineIntensive care medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: We analysed the association of mortality and prescription of antibiotics prior to hospitalization for community-acquired pneumonia. METHODS: We used administrative data (hospital abstracts, physician claims, prescriptions) for seniors (age 61 years and over) for Alberta, Canada from 1 April 1994 to 31 March 1999. RESULTS: Hospitalization of 21 191 seniors occurred during the study period. In about 43% of hospitalizations (n = 9034), a physician was consulted prior to hospital admission. Antibiotics were dispensed to 31% of those with a prior physician visit and in about 72%, the antibiotic choice was deemed appropriate. The odds for mortality were significantly decreased in those with prior physician visits (OR = 0.87, P < 0.01), with any antibiotic prescription (OR = 0.66, P < 0.0001), and with an appropriate antibiotic (OR = 0.68, P = 0.03). The choice of an appropriate antibiotic as opposed to an inappropriate antibiotic resulted in a 2.6% absolute and 38% relative mortality reduction. CONCLUSION: Choosing an appropriate outpatient antibiotic in accordance with published expert opinion guidelines compared with inappropriate antibiotic prescriptions decreased hospital mortality in patients subsequently hospitalized for community-acquired pneumonia.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.120
GPT teacher head0.438
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations18
Published2004
Admission routes2
Has abstractyes

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