A Critical Appraisal of Sedation, Analgesia and Delirium in Neurocritical Care
Bibliographic record
Abstract
Administering analgesics, sedatives and antipsychotics is challenging in the Neurological Intensive Care Unit (NICU). We reviewed this literature and our current practice to better inform the critical care practitioner and to identify gaps for future research. We electronically searched observational, intervention and outcome studies addressing sedation, analgesia and delirium in the NICU, and their bibliographies. Practice patterns were assessed in three critical care units with specialized neurological care in Montreal. Bedside pain assessment tools are psychometrically validated in the neuro-critically ill but sedation and delirium tools are not. Rigorous pain and sedation assessments appear feasible; delirium screening has not been tested. Publications addressing outcomes and responses to pharmacologic treatment lack consistency, rigor or both. In daily practice, pharmacologic management varies greatly. Clearly, little information exists on analgesia, sedation and delirium in the NICU. Systematic evaluation of pain improves outcome. No evidence-based therapeutic recommendations can be proffered.
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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".