Therapeutic Hypothermia in Brazil: A MultiProfessional National Survey
Bibliographic record
Abstract
OBJECTIVE: To determine the rate of therapeutic hypothermia (TH) use, current practices, and long-term follow-up. STUDY DESIGN: Prospective cross-sectional national survey with 19 questions related to the assessment of hypoxic-ischemic encephalopathy (HIE) and TH practices. An online questionnaire was made available to health care professionals working in neonatal care in Brazil. RESULTS: A total of 1,092 professionals replied, of which 681 (62%) reported using TH in their units. Of these, 624 (92%) provided TH practices details: 136 (20%) did not use any neurologic score or amplitude-integrated electroencephalogram (aEEG) to assess encephalopathy and 81(13%) did not answer this question. Any specific training for encephalopathy assessment was provided to only 81/407 (19%) professionals. Infants with mild HIE are cooled according to 184 (29%) of the respondents. Significant variations in practice were noticed concerning time of initiation and cooling methods, site of temperature measurements and monitoring, and access to aEEG, electroencephalogram (EEG), and neurology consultation. Only 19% could perform a brain magnetic resonance imaging (MRI), and 31% reported having a well-established follow-up program for these infants. CONCLUSION: TH has been implemented in Brazil but with significant heterogeneity for most aspects of hypothermia practices, which may affect safety or efficacy of the therapy. A step forward toward quality improvement is important.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".