Gaps in Medical and Device Therapy for Patients with Left Ventricular Systolic Dysfunction: The EchoGap Study
Bibliographic record
Abstract
OBJECTIVES: To assess gaps between guidelines and medicine prescription/dosing and referral for defibrillator therapy in patients with left ventricular systolic dysfunction (LVSD). METHODS: Outpatient echocardiography reports at an academic hospital centre were screened and outpatients with LVEF<40% were included. A questionnaire was mailed to the patients' physician, querying prescription/dosing of ACE-inhibitors (ACEi), angiotensin receptor blockers (ARB) and beta-blockers (BB). Patients with LVEF<30% had additional questions on implantable cardiac defibrillator (ICD) referral. RESULTS: Mean age was 69.6+/-12.2 years and mean LVEF was 29.7+/-6.5%. ACEi and/or ARB prescription rate was 260/309(84.1%) versus 256/308(83.1%) for BB (p=NS for comparison). Of patients on ACEi, 77/183(42.1%) were on target dose, compared to 7/45(15.5%) for ARB and 9/254(3.5%) for BB (p<0.01). Of 171/309 patients (55.3%) with LVEF<30%, 72/171(42.1%) had an ICD and 16/171(9.4%) were referred for one. CONCLUSION: Prescription rates of evidence-based HF medicines are relatively high in outpatients with LVSD referred for echocardiography at this Canadian academic medical centre; however, the proportion of patients at target doses was modest for ACEi and low for ARB and BB. Approximately half of patients who qualify for ICD by EF alone have one or were referred. Important reasons for patients with LVSD not on evidence-based therapy were identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".