End-of-Life in pediatric e neonatal intensive care unit : medical practices before death in a reference pediatric hospital at brazilin northeast
Bibliographic record
Abstract
Changes around the world at the XX century allowed new technologies to improve the possibilities of surviving in so many cases of sickness that were incurable before. Besides the benefits it brought, other consequences have come together specially futility, in other words futile therapeutic measures when the cure is impossible and the death is inevitable. These facts have influence in client satisfaction, lengh of stay and costs elevation. Since 1990 life support limitation (LSL) started to be considered in Europe, United States, Canada and Austrlia to assist patients in terminal conditions to whom curative practices wouldn’t bring any benefit. In Brazil there is a limited number of studies and the data from the Northeastern are even less. The aim of this study was to describe the medical practices before death in patients in PICU and NICU at a reference pediatric hospital in Brazilian Northeastern. Data were collected from 86 medical charts. Only 3.5% of LSL was registered. 33.7% of patients had some chronic disease and neoplasic ones were more comons. The main causes of death were: sepsis (23.5%), MDOS (18.8%), respiratory failure (12.9%), congenital heart disease (8.2%) and the other causes 36.6% together. Most of patients died after increment in vasoatives administration, considering the final 24 hours before death (59.3% 24h before, 70.9% at the momento of death). Mechanical ventilation (89.5% 24h before, 95.2% at the moment). In the other hand palliative care and pain control were not so frequent as could be expected: sedative (39.5 24h before, 43% at the moment) analgesic (60% 24h before, 60.5% at the moment). CPR was offered in 4.7% of patients 48h before death, 29.1% 24h before and 69.4% at the moment of death. Adrenaline was used in 55.4% at the moment of death. These data show that LSL is not a frequente alternative to assist terminal patients at PICU and NICU in Brazilian Northeastern, where more prevalent practices are to maintain life support instead of offer palliative care and pain control. At Southern and Southeastern practice include LSL and palliative care more frequently, what suggests better practices of the end-of-life care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".