Merits and perils of targeted neonatal echocardiography-based hemodynamic research: a position statement
Bibliographic record
Abstract
In the neonatal setting, point-of-care ultrasound is increasingly being used to help clinicians with the evaluation of heart function. Practices in neonatology, particularly with regard to acute and chronic hemodynamic managements, were traditionally more driven on dogma and predefined thresholds and not always supported by demonstrable physiology. For the first time, targeted neonatal echocardiography (TNE) provided neonatal intensivists with a bedside tool that made real-time assessment of neonatal hemodynamics status feasible in even the tiniest of babies. This opened the door towards more targeted physiological driven practices, allowing us to test historical approaches to clinical problems in a more precise way. Despite the standardization of TNE training and the creation of a formalized curriculum, little attention has been paid to the establishment of an empirical framework to adjudicate scientific investigation. In this position statement, we reflect on the evolution of TNE in Canadian neonatal intensive care units, appraise its strengths and limitations, and suggest guiding principles for clinicians and researchers to consider as they take this field forward.
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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 | 0.037 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".