Analgesia in Neurocritical Care
Bibliographic record
Abstract
OBJECTIVE: To characterize analgesic administration in neurocritical care. DESIGN: ICU pharmacy database analgesic delivery audits from five countries. A 31-question analgesic agent survey was constructed, validated, and e-distributed in four countries. SETTING: International multicenter neuro-ICU database audit and electronic survey. PATIENTS: Six ICUs provided individual, anonymized analgesic delivery data in primary neurological diagnosis patients. Prescriber surveys were disseminated by neurocritical care societies. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Analgesic delivery data from 173 patients in French, Canadian, American, and Australian and New Zealand ICUs suggest that acetaminophen/paracetamol is the most common first-line analgesic (49.1% of patients); opiates are the "second line" in 31.5% of patients; however, 33% patients received no second agent. In the 2.3% with demyelinating disease, gabapentin was the most likely second analgesic (50.0%). Third-line analgesics were scarce across sites and neuropathologies. Few national or regional differences were found. The analgesic preference rankings noted by the 95 international physicians who completed the survey matched the audits. However, self-reported analgesic prescription rates were much higher than pharmacy records indicate, with self-reported prescribing of both acetaminophen/paracetamol and opiates in 97% of patients and gabapentin in 45% of patients. Third-line analgesic variability appeared to be driven by neuropathology; ibuprofen was preferred for traumatic brain injury, postcraniotomy, and thromboembolic stroke patients, whereas gabapentin/pregabalin were favored in subarachnoid hemorrhage, intracranial hemorrhage, spine, demyelinating disease, and epileptic patients. CONCLUSIONS: Opiates and acetaminophen are preferred analgesic agents, and gabapentin is a contextual third choice, in neurocritically ill patients. Other agents are rarely prescribed. The discordance in physician self-reports and objective audits suggest that pain management optimization studies are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".