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A pilot protocol for surveillance of infection and antibiotic prescribing in primary healthcare across the globe: Antibiotic Prescribing in Primary Healthcare Point Prevalence Survey (APC-PPS)

2025· preprint· en· W4406664231 on OpenAlexafffund
Aislinn Cook, Jan Goelen, Catrin E. Moore, Jennifer Martin, Koen B. Pouwels

Bibliographic record

VenueWellcome Open Research · 2025
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsInstitute of Infection and Immunity
FundersInstitute of Infection and ImmunitySt. George's, University of LondonWellcome Trust
KeywordsMedicineMedical prescriptionHealth careAntibioticsFamily medicineProtocol (science)Antibiotic resistanceAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

Little data is available from the primary healthcare setting in low- and middle-income countries to describe the burden of clinical infections and antibiotic prescribing proportions for those infections. The AWaRe Antibiotic Book provides a framework for assessing antibiotic prescribing in primary healthcare but requires understanding both frequency of clinical infections and their antibiotic prescribing proportions. The Antibiotic Prescribing in Primary Healthcare Point Prevalence Survey (APC-PPS) project is a series of point prevalence surveys conducted at primary healthcare facilities in LMICs to capture the frequency of consultation for different clinical infections and diagnoses and the frequency and type of antibiotic prescribing associated with these infections in primary healthcare facilities. This study aims to assess the feasibility of using a PPS methodology to collect data on clinical presentation and antibiotic prescribing in primary healthcare settings. The data collected are necessary to be able to summarise relative frequencies of presentation of different clinical infections and antibiotic prescribing practices to inform global estimates of antibiotic use and inform the development of surveillance methods and representative sampling frames. Each site will conduct 6-8 point prevalence surveys over the course of 12 months. Completely anonymous data on age, sex, relevant comorbidities, infection symptoms and diagnoses and antibiotic prescription are collected for patients of all ages with acute infection symptoms (up to 14 days of symptoms) who present to the facility on the day of the survey. No identifiable data will be collected from individuals. Data is collected via ODK Collect and stored in a secure ODK Cloud server hosted by City St. George's, University of London. Sites will be active between early 2023- mid 2025, with regular interim data analysis scheduled and final data analysis planned by mid 2026. All required local and national ethical and regulatory approvals will be obtained prior to sites starting.

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)0.018
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.006
Research integrity0.0010.004
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 head0.154
GPT teacher head0.419
Teacher spread0.264 · 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 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

Citations6
Published2025
Admission routes2
Has abstractyes

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