Permissive hypotension in the extremely low birthweight infant with signs of good perfusion
Bibliographic record
Abstract
INTRODUCTION: Many practitioners routinely treat infants whose mean arterial blood pressure in mm Hg is less than their gestational age in weeks (GA). OBJECTIVE: To assess the effectiveness of utilising a combined approach of clinical signs, metabolic acidosis and absolute blood pressure (BP) values when deciding to treat hypotension in the extremely low birthweight (ELBW) infant. METHODS: Retrospective cohort study of all live born ELBW infants admitted to our neonatal intensive care unit over a 4-year period. Patients were grouped as either normotensive (BP never less than GA), hypotensive and not treated (BP<GA but signs of good perfusion; we termed this permissive hypotension) and hypotensive treated (BP<GA with signs of poor perfusion). RESULTS: 118 patients were admitted during this period. Blood pressure data were available on 108 patients. 53% of patients were hypotensive (mean BP in mm Hg less than GA in weeks). Treated patients had lower birth weight and GA, and significantly lower blood pressure at 6, 12, 18 and 24 h. Normotensive patients and patients designated as having permissive hypotension had similar outcomes. Mean blood pressure in the permissive group increased from 26 mm Hg at 6 h to 31 mm Hg at 24 h. In a logistic regression model, treated hypotension is independently associated with mortality, odds ratio 8.0 (95% CI 2.3 to 28, p<0.001). CONCLUSIONS: Blood pressure spontaneously improves in ELBW infants during the first 24 h. Infants hypotensive on GA criteria but with clinical evidence of good perfusion had as good an outcome as normotensive patients. Treated low blood pressure was associated with adverse outcome.
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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 | 0.000 | 0.002 |
| 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".