An Echocardiographic Study of the Limitations of the Tei Index in Aortic Stenosis
Bibliographic record
Abstract
BACKGROUND: The omnibus Tei index, the sum of isovolumic contraction and relaxation times divided by the ejection time, is a good indicator of global cardiac function. However, these time intervals can be influenced by valvular heart disease and the role of the Tei index among patients with aortic stenosis (AS) and left ventricular systolic dysfunction remains incompletely defined. METHODS: Doppler time intervals were measured retrospectively by two observers, and the Tei index were calculated on 80 patients with various degrees of left ventricular systolic dysfunction and severity of AS. Differences between observers were resolved by consensus. Ejection fraction (EF) was visually estimated, and the aortic valve area (AVA) was calculated using the continuity equation. RESULTS: The mean AVA was 1.1+/-0.5 cm(2) and the mean EF was 0.39+/-0.15. Although correlation (r = 0.61, P < 0.001) and agreement (intraclass correlation coefficient=0.55) for the Tei index were satisfactory, there were significant differences between observers (P < 0.001). Using consensus data, the mean Tei index was 0.32+/-0.20 and significantly lower among patients with severe compared with less severe AS (P=0.01). The index varied inversely with left ventricular function (P=0.003). However, receiver operating characteristic analysis shows that the Tei index lacks discrimination in accurately identifying poor left ventricular function or severe AS or both. CONCLUSION: The Tei index varies inversely with systolic ventricular function (low index with good EF) and positively with severity of AS (low index with smaller valve area). As such, it may have limited utility among individual patients with AS.
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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".