Physician specialty and quality of care for CHF: different patients or different patterns of practice?
Bibliographic record
Abstract
BACKGROUND: Previous reports have suggested that internists employ evidence-based care for congestive heart failure (CHF) less frequently than cardiologists. Reasons for this possible difference are unclear. METHODS: A retrospective review of 185 consecutive patients admitted to a Canadian tertiary care facility between April 1998 and March 1999 with a primary diagnosis of CHF and who were treated by internists (IM group) or cardiologists (CARD group) was conducted. RESULTS: The CARD group (n=65) was younger (70 versus 76 years, P<0.001) and had larger left ventricular end-diastolic diameter by echocardiography (57 versus 51 mm, P=0.006) than the IM group (n=120). The CARD group documented ejection fraction in 90% of cases versus 54% in the IM group (P<0.05). There was no difference in angiotensin-converting enzyme (ACE) inhibitor usage (68% versus 63%, P=0.48) or optimal ACE dosage (CARD 50% versus IM 42%, P=0.44). Multivariate predictors of ACE inhibitor usage were serum creatinine, male sex, peripheral edema and increasing serum glucose. The CARD group had higher usage of beta-blockers (69% versus 49%, P<0.009), lipid lowering medication (35% versus 17%, P<0.004) and warfarin therapy for atrial fibrillation (74% versus 28%, P<0.005). CONCLUSION: The data suggest that Canadian cardiologists and internists use ACE inhibitors equally and care for a relatively similar group of CHF patients. However, beta-blockade, warfarin, lipid lowering therapy and documentation of critical data occurred more frequently under cardiologist care. The possibility that there may be a gradation of adoption of newer guidelines for CHF care according to physician specialty is raised.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".