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Record W1976869581 · doi:10.1016/j.jegh.2014.12.002

Symptom clusters on primary care medical service trips in five regions in Latin America

2015· article· en· W1976869581 on OpenAlexaff
Christopher Dainton, Charlene H. Chu

Bibliographic record

VenueJournal of Epidemiology and Global Health · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of TorontoQueen's University
Fundersnot available
KeywordsTRIPS architectureMedicineLatin AmericansFamily medicineService (business)Medical recordGuidelineEpidemiologyPrimary careSurgeryPathology

Abstract

fetched live from OpenAlex

Short-term primary care medical service trips organized by the North American non-governmental organizations (NGOs) serve many communities in Latin America that are poorly served by the national health system. This descriptive study contributes to the understanding of the epidemiology of patients seen on such low-resource trips. An analysis was conducted on epidemiologic data collected from anonymized electronic medical records on patients seen during 34 short-term medical service trips in five regions in Ecuador, Guatemala, and the Dominican Republic between April 2013 and April 2014. A total of 22,977 patients were assessed by North American clinicians (physicians, nurse practitioners, physician assistants) on primary care, low-resource medical service trips. The majority of patients were female (67.1%), and their average age was 36. The most common presenting symptoms in all regions were general pain, upper respiratory tract symptoms, skin disorders, eye irritation, dyspepsia, and nonspecific abdominal complaints; 71-78% of primary care complaints were easily aggregated into well-defined symptom clusters. The results suggest that guideline development for clinicians involved in these types of medical service trips should focus on management of the high-yield symptom clusters described by these data.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.392
Teacher spread0.343 · 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 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

Citations21
Published2015
Admission routes1
Has abstractyes

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