THERAPEUTIC HYPOTHERMIA IN NEONATES: REVIEW OF CURRENT CLINICAL DATA, ILCOR RECOMMENDATIONS AND SUGGESTIONS FOR IMPLEMENTATION IN NEONATAL INTENSIVE CARE UNITS
Bibliographic record
Abstract
Recent evidence suggests that the current ILCOR guidelines regarding hypothermia for the treatment of neonatal encephalopathy need urgent revision. In 2005 when the current ILCOR guidelines were finalised one large (CoolCap trial, n = 235) and one small RCT (n = 67), in addition to pilot trials, had been published, and demonstrated that therapeutic hypothermia after perinatal asphyxia was safe. The CoolCap trial showed a borderline overall effect on death and disability at 18 months of age, but significant improvement in a large subset of infants with less severe electroencephalographic changes. Based on this and other available evidence, the 2005 ILCOR guidelines supported post resuscitation hypothermia in paediatric patients after cardiac arrest, but not after neonatal resuscitation. Subsequently, a whole body cooling trial supported by the NICHD reported a significant overall improvement in death or disability. Further large neonatal trials of hypothermia have stopped recruitment and their final results are likely to be published 2009–2011. Many important questions around the optimal therapeutic use of hypothermia remain to be answered. Nevertheless, independent meta-analyses of the published trials now indicate a consistent, robust beneficial effect of therapeutic hypothermia for moderate to severe neonatal encephalopathy, with a mean NNT between 6 and 8. Given that there is currently no other clinically proven treatment for infants with neonatal encephalopathy we propose that an interim advisory statement should be issued to support and guide the introduction of therapeutic hypothermia into routine clinical practice.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".