Subcostal TAPSE measured by anatomical M-mode: prospective reliability clinical study in critically ill patients
Bibliographic record
Abstract
BACKGROUND: Tricuspid annular plane systolic excursion (TAPSE), evaluated from a four-chamber apical view, is an echocardiographic parameter for the detection of right ventricular systolic dysfunction (RVD). We decided to assess the reliability of TAPSE measured from subcostal view (sTAPSE) by anatomical M-mode imaging (AMM) for evaluation of right ventricular systolic function and prediction of RVD in the critically ill patients by comparison with other echocardiographic parameters. METHODS: We conducted an observational, prospective clinical study in 100 patients hospitalized in the intensive care unit. TAPSE, doppler tissue imaging-derived tricuspid lateral annular systolic velocity (DTI-S' wave), two-dimensional fraction area change (2D FAC) and DTI-right ventricular index of myocardial performance (DTI-RIMP) were measured by transthoracic echocardiography. A subcostal four-chamber view was recorded for sTAPSE measurement. For that purpose, the cursor of AMM was aligned along the direction of the tricuspid lateral annulus movement and the amplitude of the movement was measured. RESULTS: In a group of patients aged 64±16 years with a 31% prevalence of RVD we identified strong correlation between TAPSE and sTAPSE (r=0.963, P<0.001). sTAPSE correlated well with other measures of right ventricular systolic function (DTI-S' wave: r=0.765; 2D FAC: r=0.701; DTI-RIMP: r=-0.661, P<0.001, respectively). The value of sTAPSE ≤15 mm predicted the presence of RVD defined by TAPSE with a sensitivity of 94.7% and specificity of 100.0%. CONCLUSIONS: The sTAPSE measured by AMM in a population of critically ill patients has been found to be a reliable parameter of right ventricular systolic function and predicted RVD with high reliability.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".