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)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".