A model of integrated lung and focused heart ultrasound as a new screening examination in infants at risk of respiratory or hemodynamic compromise
Bibliographic record
Abstract
Objective: This was a feasibility study to determine whether an educational program conducted over 2 days followed by 25 performed studies under supervision equips physicians with the skills to accurately interpret and perform integrated lung ultrasound (LUS) and focused heart ultrasound (FHUS) as a screening exam in infants at risk of respiratory or hemodynamic compromise. Methods: We conducted a training course over 2 days (total of 16 hours) to teach fellows how to interpret a pre-designed model of LUS and FHUS, as a screening exam for infants at risk of respiratory or hemodynamic compromise. Then trainees performed 25 cases with different neonatal lung and functional heart issues. The screening model included only the basic views required to evaluate common lung parenchymal and functional neonatal heart conditions in sick infants. The accuracy of interpretation during the course was assessed by Kappa. Results: The inter-rater agreement between all trainees and instructor improved on the second day of the course to Kappa 0.86 (95% CI: 0.72-0.97) for LUS views and 0.78 (95% CI: 0.69-0.91) for FHUS views. The inter-rater agreement between trainees themselves improved from Kappa 0.64 (95% CI: 0.47-0.81) for LUS on day one to 0.89 (95% CI: 0.81-0.96) on day two. And from 0.58 (95% CI: 0.44-0.73) on day one to 0.75 (95% CI: 0.68-0.84) on day two. Conclusion: Bedside screening, using integrated LUS and FHUS can be a useful adjunct to clinical examination in infants at risk of respiratory or hemodynamic compromise.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".