What are the most common conditions in primary care? Systematic review.
Bibliographic record
Abstract
OBJECTIVE: To identify the most commonly presenting conditions in primary care globally, and to compare common reasons for visits (RFVs) as reported by clinicians and patients, as well as among countries of different economic classifications. DATA SOURCES: Twelve scientific databases were searched up to January 2016, and a dual independent review was performed to select primary care studies. STUDY SELECTION: Studies were included if they contained 20 000 visits or more (or equivalent volume by patient-clinician interactions) and listed 10 or more RFVs. Dual independent data extraction of study characteristics and RFV rankings was performed. Data analysis was descriptive, with pooled rankings of RFVs across studies. SYNTHESIS: Eighteen studies met inclusion criteria (median 250 000 patients or 83 161 visits). Data were from 12 countries across 5 continents. The 10 most common clinician-reported RFVs were upper respiratory tract infection, hypertension, routine health maintenance, arthritis, diabetes, depression or anxiety, pneumonia, acute otitis media, back pain, and dermatitis. The 10 most common patient-reported RFVs were symptomatic conditions including cough, back pain, abdominal symptoms, pharyngitis, dermatitis, fever, headache, leg symptoms, unspecified respiratory concerns, and fatigue. Globally, upper respiratory tract infection and hypertension were the most common clinician-reported RFVs. In developed countries the next most common RFVs were depression or anxiety and back pain, and in developing countries they were pneumonia and tuberculosis. There was a paucity of available data, particularly from developing countries. CONCLUSION: There are differences between clinician-reported and patient-reported RFVs to primary care, as well as between developed and developing countries. The results of our review are useful for the development of primary care guidelines, the allocation of resources, and the design of training programs and curricula.
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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 | 0.010 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".