ACHADOS ALTERADOS NO ATENDIENTO PRIMÁRIO DA QUEIMADURA
Bibliographic record
Abstract
Liver transplantation is a life-saving opportunity for patients with end-stage liver diseases worldwide.There are two types of liver transplants, each with its own challenges.Even patients with brain death can become donors.Both types involve preserving and reconstructing vital connections of this essential organ.Advances in this field offer hope for those with advanced liver diseases.An analysis of liver transplants in Rio de Janeiro over 14 years aims to correlate current epidemiology with outcomes.Data from DATASUS from January 2008 to December 2022 were reviewed, including admissions, public expenditure, complexity, mortality, deaths, length of stay, and care, as well as articles from Scielo, Lilacs, and PubMed.There were 1,631 admissions, costing R$146,798,794.70, with a peak in 2022 in terms of both admissions and expenditure (R$17,410,555.36).Of these, 237 procedures were elective and 1,394 were urgent, with 326 in the public sector, 150 in the private sector, and 1,155 of unknown origin, all considered high complexity.The overall mortality rate was 11.89%, with 194 deaths recorded, the highest rate being in 2010 (25.93%) and the lowest in 2020 (6.25%).Mortality was higher in elective procedures (12.24%) and in the public sector (19.94%) compared to urgent procedures (11.84%) and the private sector (14.00%), while procedures of unknown origin had a mortality rate of 9.35%.The average length of hospital
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".