P.081 Critical illness neuropathy and clinical correlates in severely burned patients
Bibliographic record
Abstract
Background: Reported incidence of critical illness neuropathy (CIN) in burn patients is between 7-41%. Methods: Retrospective review including patients admitted to quaternary ICU for burn injuries between 2010-16. Variables include demographics, burn and neurologic characteristics, EMG reports, and measurements of illness severity. Patients with and without neuropathies were compared. Results: Of 147 patients admitted to ICU, thirteen had EMG studies and eight met CIN criteria. Five had electrophysiological CIN evidence, three had clinical diagnosis. Six EMGs focused on upper limb injuries only, insufficient to diagnose CIN. One patient was diagnosed with critical illness myopathy and nine had superimposed focal mononeuropathies or plexopathy. CIN patients had a mean of larger burns (TBSA 63% vs 21%), more operations (8 vs 2) and escharotomies performed (63% vs 12%), longer ICU admissions (23 vs 9 days), longer ventilation (28 vs 8 days), higher revised Baux score (101 vs 76) and initial APACHE II scores (21 vs 15) than those without. Conclusions: CIN was identified in 5.4% of burn patients admitted to ICU, lower than previously reported in literature, and associated with higher illness severity. CIN may be under recognized if not screened for. Unit examinations should include screening neurological measures and indicated EMGs to evaluate for CIN.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".