Prediction and Outcome of Intensive Care Unit-Acquired Paresis
Bibliographic record
Abstract
BACKGROUND: Intensive care unit-acquired paresis (ICUAP) is associated with poor outcomes. Our objective was to evaluate predictors for ICUAP and the short-term outcomes associated with this condition. METHODS: A secondary analysis of a prospective study including 4157 mechanically ventilated adults in 494 intensive care units from 39 countries. After sedative interruption, patients were screened for ICUAP daily, which was defined as the presence of symmetric and flaccid quadriparesis associated with decreased or absent deep tendon reflexes. A multinomial logistic regression was used to create a predictive model for ICUAP. Propensity score matching was used to estimate the relationship between ICUAP and short-term outcomes (ie, weaning failure and intensive care unit [ICU] mortality). RESULTS: Overall, 114 (3%) patients had ICUAP. Variables associated with ICUAP were duration of mechanical ventilation (relative risk ratio [RRR] per day, 1.10; 95% confidence interval [CI] 1.08-1.12), steroid therapy (RRR 1.8; 95% CI, 1.2-2.8), insulin therapy (RRR 1.8; 95% CI 1.2-2.7), sepsis (RRR 1.9; 95% CI: 1.2 to 2.9), acute renal failure (RRR 2.2; 95% CI 1.5-3.3), and hematological failure (RRR 1.9; 95% CI: 1.2-2.9). Coefficients were used to generate a weighted scoring system to predict ICUAP. ICUAP was significantly associated with both weaning failure (paired rate difference of 22.1%; 95% CI 9.8-31.6%) and ICU mortality (paired rate difference 10.5%; 95% CI 0.1-24.0%). CONCLUSIONS: Intensive care unit-acquired paresis is relatively uncommon but is significantly associated with weaning failure and ICU mortality. We constructed a weighted scoring system, with good discrimination, to predict ICUAP in mechanically ventilated patients at the time of awakening.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".