Symptom clusters on primary care medical service trips in five regions in Latin America
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".