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Record W3006943567 · doi:10.3138/jammi.2019-0023

Defining appropriate antibiotic prescribing in primary care: A modified Delphi panel approach

2020· article· en· W3006943567 on OpenAlexaffvenueabout
Julie Hui‐Chih Wu, Bradley J. Langford, Rita Ha, Gary Garber, Nick Daneman, Jennie Johnstone, Warren J. McIsaac, Sally Sharpe, Karen Tu, Kevin L. Schwartz

Bibliographic record

VenueJournal of the Association of Medical Microbiology and Infectious Disease Canada · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsSinai Health SystemNorth York General HospitalPublic Health OntarioRegent Park Community Health CentreToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineSinusitisAntibioticsAntibiotic resistanceOtitisAntimicrobial stewardshipIntensive care medicineInternal medicinePneumoniaAntimicrobialAmoxicillinPediatricsSurgery

Abstract

fetched live from OpenAlex

Background: Antimicrobial overuse contributes to antimicrobial resistance. In the ambulatory setting, where more than 90% of antibiotics are dispensed, there are no Canadian benchmarks for appropriate use. This study aims to define the expected appropriate outpatient antibiotic prescribing rates for three age groups (<2, 2-18, >18 years) using a modified Delphi method. Methods: We developed an online questionnaire to solicit from a multidisciplinary panel (community-academic family physicians, adult-paediatric infectious disease physicians, and antimicrobial stewardship pharmacists) what percentage of 23 common clinical conditions would appropriately be treated with systemic antibiotics followed with in-person meetings to achieve 100% consensus. Results: The panelists reached consensus for one condition online and 22 conditions face-to-face, which took an average of 2.6 rounds of discussion per condition (range, min-max 1-5). The consensus for appropriate systemic antibiotic prescribing rates were, for pneumonia, pyelonephritis, non-purulent skin and soft tissue infections (SSTI), other bacterial infections, and reproductive tract infections, 100%; urinary tract infections, 95%-100%; prostatitis, 95%; epididymo-orchitis, 85%-88%; chronic obstructive pulmonary disease, 50%; purulent SSTI, 35%-50%; otitis media, 30%-40%; pharyngitis, 18%-40%; acute sinusitis, 18%-20%; chronic sinusitis, 14%; bronchitis, 5%-8%; gastroenteritis, 4%-5%; dental infections, 4%; eye infections, 1%; otitis externa, 0%-1%; and asthma, common cold, influenza, and other non-bacterial infections (0%). (Note that some differed by age group.). Conclusions: This study resulted in expert consensus for defined levels of appropriate antibiotic prescribing across a broad set of outpatient conditions. These results can be applied to community antimicrobial stewardship initiatives to investigate the level of inappropriate use and set targets to optimize antibiotic use.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.189
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1890.136
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0050.005
Scholarly communication0.0040.004
Open science0.0030.013
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations15
Published2020
Admission routes3
Has abstractyes

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