Early Pupil Abnormality Frequency Predicts Poor Outcomes and Enhances International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) Model Prognostication in Traumatic Brain Injury
Bibliographic record
Abstract
IMPORTANCE: In patients with traumatic brain injury (TBI), baseline pupillary assessment is routine; however, the occurrence rate and clinical significance of pupil abnormalities over the early course of hospitalization remain poorly characterized. OBJECTIVES: To determine whether the occurrence and frequency of pupil abnormalities within the first 72 hours of ICU admission are associated with unfavorable discharge outcomes and to assess whether incorporating this frequency improves the performance of an established prognostic model. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective observational study of adults admitted with a primary diagnosis of TBI to a single tertiary care ICU between 2018 and 2022. Inclusion criteria included at least three quantitative pupillometry assessments within the first 72 hours. MAIN OUTCOMES AND MEASURES: Quantitative pupillometry was used to calculate the Neurological Pupil index (NPi) at each assessment. Abnormalities were defined as NPi less than 3 in either eye, NPi asymmetry greater than or equal to 0.7, or pupil size asymmetry greater than or equal to 1 mm. The primary outcome was unfavorable discharge disposition (death, hospice, or long-term care). Multivariable logistic regression was used to evaluate the association between pupil abnormality frequency and outcomes, and model performance was compared using goodness-of-fit tests with and without pupil frequency added to the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. RESULTS: Among 131 patients (median age, 59 yr; 30% women), 35% had an unfavorable discharge disposition. Pupil abnormalities occurred in 60% of mild, 61% of moderate, and 88% of severe TBI patients. For each 1% increase in the frequency of pupil abnormalities over 72 hours, the odds of unfavorable discharge increased by 3% (odds ratio, 1.03; 95% CI, 1.01-1.05). Adding pupil abnormality frequency to the IMPACT model improved its goodness-of-fit (χ2 = 5.24; p = 0.02). CONCLUSIONS AND RELEVANCE: Pupil abnormalities are common across TBI severities, particularly in severe cases. A higher frequency of abnormal pupil measurements within the first 72 hours is associated with unfavorable outcomes and significantly enhances the predictive performance of established TBI prognostic models. Serial quantitative pupillometry may offer clinically valuable, dynamic prognostic information in the acute care setting.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".