Incidence of left ventricular dysfunction in the general population of the Amazon Basin and diagnostic potential of lung ultrasound
Bibliographic record
Abstract
Abstract Background B-lines by lung ultrasound (LUS) are associated with left ventricular (LV) dysfunction and may be useful for diagnostics in resource-limited settings. Purpose To assess the incidence of LV dysfunction in the general population of the Amazon Basin and to evaluate the diagnostic potential of LUS. We defined LV dysfunction as LV ejection fraction <45% and LUS was assessed across eight chest zones. B-lines were calculated as the sum of all zones. Methods and results We conducted echocardiography and LUS in 514 individuals from a population sample in the Amazon Basin (mean age 41 years, 40% men). No patients had known heart failure, renal insufficiency or lung disease. A total of 14 individuals (3%) had LV dysfunction, corresponding to an incidence of 27.2 per 1,000 individuals. The mean number of B-lines in the general population was 0.2 (range 0 to 17 B-lines). Individuals with LV dysfunction presented with a significantly higher number of B-lines (P=0.006) and more frequently had >=3 B-lines (8% vs 2%, P=0.018) compared to those with normal LV function. Identification of >=3 B-lines yielded a sensitivity of 58% and specificity of 93% (AUC 0.72, negative predictive value 99%, positive predictive value 19%) for detecting LV dysfunction. Per 1 unit increase in B-lines, LV ejection fraction decreased by 9% (95%CI: -15% to -3%, P=0.010) and the relationship persisted to be significant after adjusting for age, sex, body mass index, heart rate and creatinine (-8%, 95%CI: -15% to -2%, P=0.016). Conclusion The incidence of LV dysfunction in the Amazon Basin was 27.2/1,000 individuals and B-lines by LUS was able to detect LV dysfunction with reasonable diagnostic accuracy. Moreover, a higher number of B-lines was associated with decreasing LV ejection fraction. Our findings indicate that LUS may supplement conventional cardiac diagnostics for assessing LV function, especially in rural environments with restricted access to imaging tools.B-lines and LV ejection fraction
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".