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Record W2915200597 · doi:10.1136/bmjgh-2018-001093

e-PC101: an electronic clinical decision support tool developed in South Africa for primary care in low-income and middle-income countries

2019· article· en· W2915200597 on OpenAlexaff
Matthew Yau, Venessa Timmerman, Merrick Zwarenstein, Pat Mayers, Ruth Cornick, Eric Bateman, Lara Fairall

Bibliographic record

VenueBMJ Global Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsWestern UniversityUniversity of Toronto
FundersUniversity of Cape TownTeva Pharmaceutical IndustriesRegeneron PharmaceuticalsMedical Research CouncilSouth African Medical Research CouncilSanofiGlaxoSmithKlineAstraZeneca
KeywordsChecklistLow and middle income countriesClinical decision support systemPsychological interventionDecision support systemPrimary careHigh income countriesDeveloping countryHealth carePrimary health careMedicineProcess (computing)Digital healthNursingFamily medicineEnvironmental healthPsychologyComputer scienceEconomic growthData mining

Abstract

fetched live from OpenAlex

Health technology is increasingly recognised as a feasible method of addressing health needs in low and middle-income countries (LMICs). Primary Care 101, now known as PACK (Practical Approach to Care Kit), is a printed, algorithmic, checklist-based, comprehensive clinical decision support tool. It assists clinicians with delivering evidence-based medicine for common primary care presentations and conditions. These assessment and treatment guides have been adopted widely in primary care clinics across South Africa. This paper focuses on the process of designing, developing, and implementing a digital version of the clinical decision support tool for use on a tablet computer. Lessons learnt throughout its development and pilot implementation could apply to the creation of electronic health interventions and the digitisation of clinical tools in LMICs.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.047
GPT teacher head0.466
Teacher spread0.419 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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