International survey on diagnosis and management of hypotension in extremely preterm babies
Bibliographic record
Abstract
UNLABELLED: Hypotension is a commonly diagnosed and treated complication of extremely low gestational age newborns (ELGAN), but enormous variation in diagnosis, management and clinical practice has been documented. We sought to evaluate practice regarding the management of hypotension in ELGANs and developed a web-based questionnaire addressing diagnosis, intervention thresholds and modes of treatment of hypotension in ELGANs. We received 216 completed questionnaires from respondents in 38 countries. Most responses (83 %) were from specialist units where, together, over 26,000 very low birth weight (VLBW) infants are cared for annually. The majority (73 %) defined hypotension as a mean blood pressure (BP) in mmHg less than the gestational age in weeks. Sixty percent assessed the circulation with additional methods; echocardiography was the most commonly used (74 %), with left ventricular output and fractional shortening the two most common measurements made. The majority (85 %) used volume administration as the initial intervention. Dopamine was the inotrope most commonly used initially (80 %). If the initial inotrope therapy failed, dobutamine was the most popular second-line treatment (28 %). Delayed cord clamping was used at 51 % of the centres. CONCLUSION: The definition of hypotension in ELGANs continues to follow traditional standards. Functional echocardiography is now used to assess the circulation at many centres. Volume expansion and dopamine remain the most frequently used therapies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".