Most common diseases diagnosed in primary care in Stockholm, Sweden, in 2011
Bibliographic record
Abstract
BACKGROUND: The most commonly reported diagnoses in primary care are useful to identify and meet health care needs in society. We estimated the rates of the most common diagnoses in primary health care in total and also by gender. METHODS: This was a cross-sectional study including all 2.0 million inhabitants living in Stockholm County, Sweden, on 1 January 2009. Data on all health care appointments made in primary care in 2011 and during 2009-11 were extracted from the Stockholm County Council data warehouse VAL (Vårdanalysdatabasen; Stockholm regional health care data warehouse). Primary care data were analysed by underlying population and age. Appropriate specialist open care and inpatient data were used for comparison. RESULTS: The five most common diagnoses in primary care (in 2011) were acute upper respiratory tract infections (6.0% of the population), essential hypertension (5.6%), coughing (2.6%), dorsalgia (2.6%) and acute tonsillitis (2.4%). Female-to-male ratios were higher for 27 of the 30 most common diagnoses, the exceptions being type 2 diabetes, unspecified types of diabetes and multiple wounds. CONCLUSIONS: The 30 most common diagnoses in primary care reflect the complexity of disorders cared for in the first line of health care. Knowledge of these patterns is important when aiming at using primary health care resources in a proper way.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".