Physician-adherence to pharmacotherapy guidelines for chronic heart failure in a tertiary health facility in Lagos, Nigeria
Bibliographic record
Abstract
Background: The increasing need for adherence evaluation of CHF amongst senior physicians in our environment prompted this study. Objective: To determine physician-adherence to pharmacotherapy guidelines in CHF in an economically resource-poor tertiary health facility. Methods: Review of prescription pattern of anti-CHF drug-class of 100 confirmed systolic-CHF patients was carried out. Data for adherence-evaluation were obtained from follow-up information from out-patient clinic-notes, while data on acute care medications and precipitating factors were from in-patient hospitalization notes. Results: CHF patients aged 54.7 ± 14.5 years, had NYHA III/IV symptoms (47%) and hypertension (61%). Anti-CHF pharmacotherapy averaged three drug-types; and consisted of ACEI/ARB (83%), β blockers-BB (48%), aldosterone antagonists (41%), CG (82%), and diuretics (75%). Adherence was assessed as good or complete in 50%, partial/ incomplete in 33%; but non-adherent in 17% of the total. While overall physician-adherence was 59.6% on single drug-classes, survival- advantage combinations with ACEI/ARB+BB and ACEI/ARB+BB+AA were present in 40% and 16% respectively. Older patients (≥ 65 years) had significantly lower prescriptions of all three classes of survival advantage anti-HF drugs, as follows: ACEI/ARB (56% versus 95%); BB (37.5% versus 52%); and AA (31% versus 63%) [p < .05]. Conclusion: BB and AA were under-prescribed. Physician-adherence to evidence-based anti-HF drug classes was variable and influenced by patient’s age. It was also comparable with reports from other countries. Our physicians will benefit from a structured HF education and feed-back program.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".