Primary Care Physician Visits by Patients With Incident Hypertension
Bibliographic record
Abstract
BACKGROUND: Access to a primary care physician (PCP) improves health outcomes among patients with hypertension. The study objective was to compare PCP use among patients with incident hypertension with and without comorbidities. METHODS: Hypertensive patients newly diagnosed between April 1, 1998 and March 31, 2009 were identified using Alberta administrative databases. Three comorbidity subgroups were defined: (1) none, (2) vascular risk related, and (3) unrelated. The number of PCP visits was calculated using zero-inflation Poisson regression, with time trends compared using the χ(2) test. A Cox model was used to assess the association between PCP use and clinical outcomes. RESULTS: Of 456,263 newly diagnosed hypertensive patients (mean age, 57.6 years; 50.6% men; 62.5% no comorbidity), 88% had seen a PCP in the year before diagnosis, and 94% had seen a PCP in the year after being diagnosed. Compared with before diagnosis, the mean number of PCP visits increased after diagnosis (none, 3.95 vs 6.15; vascular risk related, 6.45 vs 7.99; and unrelated, 6.76 vs 8.24). Over the study period, the frequency of PCP visits before diagnosis was constant, and there was a statistically significant decline in the adjusted mean number of visits after diagnosis. Those with higher PCP use were less likely to die but more likely to be hospitalized regardless of comorbidity. CONCLUSIONS: The frequency of PCP visits was high before and after diagnosis. Increased PCP use was associated with a lower risk of death; however, it does increase the costs of caring for patients with hypertension. Therefore, future studies are necessary to determine the optimal level required to achieve cost-effective use of PCP resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".