Hypothermia: An Evolving Treatment for Neonatal Hypoxic Ischemic Encephalopathy
Bibliographic record
Abstract
To the Editor.— It has always been challenging to know when new therapies should be considered ready for use in practice. History has provided many contrasting examples of simple and effective treatments (such as phototherapy and antenatal steroids) that languished for decades before being adopted and treatments that were and often continue to be used well after they proved to be either useless or less effective than simpler alternatives. However, it is extremely difficult to understand why Kirpalani and colleagues1 are so concerned that some neonatologists are now choosing to offer therapeutic hypothermia on a compassionate basis. Neither these practitioners nor any official body have, to our knowledge, declared that hypothermia should be the standard of care. They, and several of the undersigned, helped develop the consensus of the 2005 National Institute of Child Health and Human Development workshop that hypothermia is an evolving (not unproven or experimental) therapy, with many questions around its optimal use.2 Thus, the underlying premise of their commentary is shaky. Three independent …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 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".