HEART RATE CHANGES ON DAY ONE IN PRETERM INFANTS AND LENGTH OF STAY IN THE NICU: A PILOT STUDY
Bibliographic record
Abstract
Objective To investigate if a statistical model of heart rate changes in preterm infants below 32 weeks gestation correlates with illness severity score and NICU LOS. Design/Methods Infants 150 bpm. The data was processed using MATLAB 7.5 (Mathworks, Natick MA) software. The probability of these infants remaining in each state and changing from one state to another was calculated as a Markov chains probability model. SNAP-II, Perinatal Extension, Version II (SNAPPE-II) and Transport Risk Index of Physiologic Stability (TRIPS) scores were calculated. Results 45 infants (21 females) with mean (SD) gestational age of 28.7 (2) weeks, mean (SD) birthweight of 1250 (346) g had a mean (SD) length of stay of 43 (45) days. None of the infants died. The strongest correlations were between length of stay and the change in states from S3–S4 (coefficient 0.52, p = 0.0003), and SNAPPE-II (coefficient 0.54, p = 0.0001). SNAP-II and TRIPS were not significantly correlated to LOS. Conclusions Although the sample size is small, probability modeling using Markov chains could prove promising to correlate LOS with HR variations of preterm infants in the first day of life.
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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 | 0.002 | 0.009 |
| 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".