Lung ultrasound predicts histological lung injury in a neonatal model of acute respiratory distress syndrome
Bibliographic record
Abstract
Abstract Rationale Point‐of‐care ultrasound (POCUS) is used to evaluate pulmonary edema in adults with acute respiratory distress syndrome (ARDS). Its use has not been validated in neonatal models. Objectives We compared an in vivo lung ultrasound score against clinical and histological markers of acute lung injury, in a neonatal animal model, hypothesizing that POCUS would sensitively diagnose early acute lung injury in neonates and discern its severity. Methods Fifteen anesthetized, ventilated 3‐day‐old neonatal piglets were divided into controls, moderate lung injury, or severe lung injury by graded treatment with oleic acid. Degree of lung injury was quantified at baseline, immediately after oleic acid administration, and 1 hour after the evolution of acute lung injury, by blood gases, ventilation parameters and calculated oxygenation deficit; hemodynamic indices by echocardiography, and lung ultrasound obtained in an 8‐region grid of anterior and posterior zones, semi‐quantitatively analyzed by a blinded observer. Lungs were inflation‐fixed postmortem at last mean airway pressure, for histological assessment. Results Acute lung injury manifested in oleic acid‐treated groups as dose‐dependent capillary leak causing intravascular depletion and cardiac failure, hypoxemia with increasing intrapulmonary shunt fraction, decreased lung compliance, and resistance. Ultrasound scores of anterior regions distinguished moderate from severe injury; scores in posterior regions reached maximum values immediately after lung injury. POCUS score correlated with calculated intrapulmonary shunt fraction (R2 = .65) and with histological injury score (R2 = .61),P < .01. Conclusion We conclude that POCUS may be valuable in neonates for early quantification of acute lung injury or ARDS; and that nondependent ultrasound regions clearly distinguish severity of pulmonary edema.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".