The Swedish Primary Care Cardiovascular Database (SPCCD): 74 751 hypertensive primary care patients
Bibliographic record
Abstract
OBJECTIVE: To describe the Swedish Primary Care Cardiovascular Database, SPCCD. Design. Longitudinal data from electronic medical records, linked to national registers. Setting. 48 primary healthcare centres in urban (south-western Stockholm) and rural (Skaraborg) regions in Sweden. Subjects. Patients diagnosed with hypertension 2001-2008. MAIN OUTCOME MEASURES: Blood pressure (BP) and impact of retrieval of data on BP levels, clinical characteristics, co-morbidity and pharmacological treatment. RESULTS: The SPCCD contains 74 751 individuals, 56% women. Completeness of data ranged from > 99% for drug prescriptions to 34% for smoking habits. BP was recorded in 98% of patients during 2001-2008 and in 63% in 2008. Mean BP based on the last recorded value in 2008 was 142 ± 17/80 ± 13 mmHg. Digit preference in BP measurements differed between the two regions, p < 0.001. Antihypertensive drugs were prescribed in primary healthcare to 88% of the patients in 2008; however, when all prescribers were included 96% purchased their drugs. Cardiovascular co-morbidity and diabetes mellitus were present in 28% and 22%, respectively. CONCLUSION: This large and representative database shows that there is room for improvement of BP control in Sweden. The SPCCD will provide a rich source for further research of hypertension and its complications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".