A quality control exercise in the echo laboratory: Reduction in inter‐observer variability in the interpretation of pulmonary hypertension
Bibliographic record
Abstract
Background Right ventricular systolic pressure (RVSP) estimated by echocardiography is critical for the initial screening and follow‐up of pulmonary hypertension (PH). Inter‐observer variability (IOV) in RVSP can impact clinical decision making. This study assessed whether a simple guideline‐based teaching intervention could reduce the IOV in RVSP interpretation. Methods and Results Eleven participants in a high‐volume tertiary level echocardiography laboratory underwent an assessment of the baseline IOV in the assessment of RVSP for a series of transthoracic echocardiograms (TTE), depicting various degrees of PH among 8 cases each before and after a teaching intervention. The inter‐observer variance (root‐mean‐square error) decreased from 26.0 mm Hg2 (5.1 mm Hg) at baseline to 5.8 mm Hg2 (2.4 mm Hg) post‐teaching intervention (P = .025). The corresponding inter‐class coefficient (ICC) increased from 0.89 to 0.98. Several factors relating to image acquisition and interpretation were identified as contributing to IOV in RVSP. The outcome was the development of a practical tool to mitigate these factors. Conclusions A simple structured teaching intervention successfully reduced IOV in the measurement of RVSP in a high‐volume echo laboratory.
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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".