Pulmonary artery dopplers for early prediction of risk for bronchopulmonary dysplasia in extremely low birth weight babies
Bibliographic record
Abstract
OBJECTIVE: The early abnormal pulmonary vasoreactivity observed in babies at risk of Bronchopulmonary dysplasia (BPD) increases the pulmonary vascular resistance. This can be assessed non-invasively using Time to Peak Velocity:Right Ventricular Ejection Time ratio (TPV:RVET) measured from pulmonary artery Doppler waveform obtained using echocardiogram. We postulate that screening for this early can predict BPD in this cohort. The objective of the study was to determine the utility of TPV:RVET in early prediction of BPD in Extremely Low Birth Weight (ELBW) babies born less than 1250grams Birth Weight. METHODS: This was a single-center retrospective cohort study of ELBW babies born<29 weeks over 4 year period who had echocardiogram between 7-21 days of life. TPV:RVET ratio was measured from pulmonary artery Doppler waveform obtained using echocardiogram. The main outcome was BPD at 36 weeks corrected gestation. The predictive ability of TPV:RVET (cut off 0.34) for subsequent development of BPD was analyzed using ROC. RESULTS: Of 589 ELBW<29 weeks, 207 babies were eligible. BPD was found in 60.4%. The TPV:RVET at 0.34 had sensitivity 76.8% (95%CI 68.4-83.9), specificity 85.4% (95%CI 75.8-92.2), positive predictive value 88.9% (95%CI 81.4-94.1), negative predictive value 70.7% (95%CI 60.7-79), and ROC area 0.811 (95%CI 0.757-0.864). Odds ratio of having BPD for TPV:RVET at 0.34 was 19.9 (95%CI 8.19-48.34) and increased by 1.07 (95%CI 1.05-1.09) with every additional days of mechanical ventilation. TPV:RVET ratio had 92.75% inter-observer agreement with kappa 0.83. CONCLUSION: TPV:RVET ratio is a good and reliable early screening tool for subsequent development of BPD in ELBW babies with substantial inter-observer agreement.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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, 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".