Monitoramento de pacientes neurocirúrgicos no pós‐operatório – utilidade dos escores de avaliação neurológica e do índice bispectral
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We examined the additive effect of the Ramsay scale, Canadian Neurological Scale (CNS), Nursing Delirium Screening Scale (Nu-DESC), and Bispectral Index (BIS) to see whether along with the assessment of pupils and Glasgow Coma Scale (GCS) it improved early detection of postoperative neurological complications. METHODS: We designed a prospective observational study of two elective neurosurgery groups of patients: craniotomies (CG) and non-craniotomies (NCG). We analyze the concordance and the odds ratio (OR) of altered neurological scales and BIS in the Post-Anesthesia Care Unit (PACU) for postoperative neurological complications. We compared the isolated assessment of pupils and GCS (pupils-GCS) with all the neurologic assessment scales and BIS (scales-BIS). RESULTS: In the CG (n=70), 16 patients (22.9%) had neurological complications in PACU. The scales-BIS registered more alterations than the pupils-GCS (31.4% vs. 20%; p<0.001), were more sensitive (94% vs. 50%) and allowed a more precise estimate for neurological complications in PACU (p=0.002; OR=7.15, 95% CI=2.1-24.7 vs. p=0.002; OR=9.5, 95% CI=2.3-39.4). In the NCG (n=46), there were no neurological complications in PACU. The scales-BIS showed alterations in 18 cases (39.1%) versus 1 (2.2%) with the pupils-GCS (p<0.001). Altered CNS on PACU admission increased the risk of neurological complications in the ward (p=0.048; OR=7.28, 95% CI=1.021-52.006). CONCLUSIONS: Applied together, the assessment of pupils, GCS, Ramsay scale, CNS, Nu-DESC and BIS improved early detection of postoperative neurological complications in PACU after elective craniotomies.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".