Differences in ventricular septal motion between subgroups of patients with heart failure.
Bibliographic record
Abstract
BACKGROUND: Septal systolic motion is determined by the end-diastolic trans-septal pressure gradient, and hence is load dependent. OBJECTIVE: To explore septal contribution to left ventricular (LV) systolic function in patients with heart failure. DESIGN: Echocardiograms were identified post hoc from normal subjects and a cohort of patients with heart failure. PATIENTS: Twelve normal subjects and 69 patients with heart failure and normal conduction or left bundle brance block (LBBB) were studied. METHODS: Parasternal short axis LV end-diastolic and end-systolic areas were traced. Using a floating centroid, 32 radial chords were constructed, and perecentage shortening from end-diastole to end-systole was calculated for each chord. MAIN RESULTS: Comparing heart failure with normal conduction and LBBB, LV end-diastolic area was similar (43+/-10 versus 45+/-12 cm(2) not significant), but stroke area was higher in normal conduction (7+/-4 versus 4+/-4cm(2), P<0.05) as was area ejection fraction (0.17+/-0.11 versus 0.10+/- 0.08, P<0.01). In normal subjects, the summed percentage shortening of 10 midseptal chords was similar to that of 10 midfreewall chords (256+/-16% versus 235+/-32%, not significant). In contrast, patients with heart failure and normal conduction had greater midseptal than midfreewall sum med chord shortening (113+/-18% versus 60+/-12%, P<0.05); patients with heart failure and LBBB had paradoxical septal motion (3+/-28, P<0.05 compared with normal conduction). CONCLUSIONS: Patients with heart failure and normal conduction have an enhanced septal contribution to LV systolic function compared with normal subjects. In heart failure with LBBB, this is lost and the area ejection fraction is lower. Strategies to optimize septal function in heart failure warrant further study.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".