Performance of a Simplified Wall Motion Score Index Method for Noncardiologists to Assess Left Ventricular Ejection Fraction
Bibliographic record
Abstract
Backgrounds. For emergency physicians performing a goal-directed transthoracic echocardiogram (TTE), a reliable estimate of LVEF must be obtained rapidly. We compared rapid LVEF estimates obtained from short axis sections to those obtained from apical sections using two methods of evaluation. Methods. The TTE's of 6 patients were interpreted by 16 echo-proficient readers (PRO group) and 105 novice readers (NOV group). LVEF was assessed by each group. The strategies consisted of either a global visual estimation (VIS) of LVEF or semiquantitative (SQ) methods. Results. Using RNV and BIP as a reference standard, NOV readers performed better with the SQ method than global visual estimation. For NOV readers, best agreement was achieved with the 234C sequence in low LVEF situations, but with the BMA series in normal LVEF settings. Neither series of views was better than the other in the setting of mild LVEF depression. Conclusion. Semi-quantitative method was superior to global visual estimation of LVEF in NOV group in most of the LVEF ranges.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".