Low blood pressure in extremely preterm infants: does treatment affect outcome?
Bibliographic record
Abstract
Do extremely immature preterm infants with blood pressure which is believed to be low have reduced systemic perfusion, reduced cerebral oxygen delivery, increased cerebral injury, an increase in acute complications of prematurity and an increase in long-term disability? If so, below what value of blood pressure do these adverse outcomes increase? These unanswered questions are of vital importance; extremely preterm infants have high rates of developmental delay and disability, blood pressures are often numerically very low and many preterm infants receive treatments which are potentially toxic with the goal of increasing their blood pressure. Unfortunately, it is not at all clear whether the common treatments for low blood pressure improve systemic flow or cerebral perfusion, or among the available options, which treatments are effective? The reason for this poverty of information is the lack of adequate trials. There are no controlled studies of hypotension therapy in the preterm newborn which include an untreated group, so changes in systemic perfusion or indirect measures of cerebral blood flow cannot necessarily be ascribed to the intervention. In addition, the small number of comparative trials that have been performed have all been vastly underpowered, and have concentrated on short-term physiological end points, usually blood pressure.1 One study which compared the effects of dopamine and epinephrine on indices of cerebral perfusion showed an increase of about 20% in cerebral blood volume after 2 h of treatment, in association with about a 50% increase in mean blood pressure, with no difference between the groups2; there was …
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".