Asociación entre el grado de control de la hipertensión arterial, la comorbilidad y los costes en personas de más de 30 años durante el año 2006
Bibliographic record
Abstract
BACKGROUND: Arterial hypertension is one of the main reasons for primary care consultations. This study is aimed at determining the relationship among the degree to which arterial hypertension is controlled, comorbidity and the direct costs in primary care. METHODS: Retrospective, multi-centre design. Subjects over 30 years of age pertaining to five primary care teams (2006) were included. CRITERIA: good control (<140/90 and <130/80 mmHg in diabetics and those with cardiovascular disease [CVD]. Main general measurements, CVD, Charlson index, casuistic/comorbidity (Adjusted Clinical Groups), clinical parameters and direct costs (fixed/semifixed and variable costs) [medications, tests and referrals]) Logic regression and ANCOVA for correcting the model, p<0.05. RESULTS: The prevalence of arterial hypertension was 26.5% (mean age: 67.1 years; males: 43.5%). Good control totalled 52.0% (CI: 51.2-52.8%). Poor control was independently related to diabetes (Odds Ratio=3.8), CVD (Odds Ratio=2.2) and males (Odds Ratio=1.2), p<0.001. The average/direct unit cost/year was 1,202.13 Euro vs. 1,183.55 Euro (p=0.032). CONCLUSIONS: Those individuals whose arterial hypertension was poorly controlled displayed a greater burden of morbidity and a similar healthcare cost in comparison to those under good control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".