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Record W2343275902 · doi:10.1183/23120541.00001-2016

Antibiotic preferences for childhood pneumonia vary by physician type and European region

2016· article· en· W2343275902 on OpenAlexfundno aff
Julia Bielicki, Charlotte Barker, Alike W. van der Velden, Mike Sharland, Diego Van Esso, Adamos Hadjipanayis, Stefano del Torso, Zachi Grossman

Bibliographic record

VenueERJ Open Research · 2016
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsnot available
FundersInstitute of Infection and ImmunityEuropean CommissionUniversity of SouthamptonSeventh Framework ProgrammeUniversity of CyprusUniversität Basel
KeywordsMedicinePrimary careMedical prescriptionPneumoniaFamily medicineAntibioticsAmbulatoryIntensive care medicineNursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Antibiotics are the most commonly prescribed medicines for acutely unwell children worldwide [1]. Many of these antibiotic prescriptions are issued in a primary care setting [2–4]. Despite this, a robust evidence base for agent selection in primary care is lacking for many childhood indications, including community-acquired pneumonia (CAP). In an era of increasing antibiotic resistance, with optimal antibiotic use being paramount to preserve this precious resource, trials involving ambulatory patients representative of those seen in primary care are needed to address this gap. Survey of EAPRASnet and @PREPARE_EUROPE members reveals heterogeneity of antibiotic choice for childhood pneumonia <http://ow.ly/4mIS2P> We would like to thank all EAPRASnet and PREPARE participants who completed the survey. We are also grateful to Lucy Yardley (University of Southampton, Southhampton, UK) for providing critical input into the survey design.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.129
GPT teacher head0.402
Teacher spread0.273 · 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 designBench or experimental
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

Citations2
Published2016
Admission routes1
Has abstractyes

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