Validation of the GCS−Pupil Scale in Traumatic Brain Injury: Incremental Prognostic Value of Pupillary Reactivity with GCS in the Prospective Observational Cohorts CENTER-TBI and TRACK-TBI
Bibliographic record
Abstract
To compare the incremental prognostic value of pupillary reactivity captured as part of the Glasgow Coma Scale–Pupils (GCS–P) score or added as separate variable to the GCS+P, in traumatic brain injury (TBI). We analyzed patients enrolled between 2014 and 2018 in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI, n = 3521) and the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI, n = 1439) cohorts. Logistic regression was utilized to quantify the prognostic performances of GCS–P (GCS minus number of unreactive pupils) and GCS+P versus GCS alone according to Nagelkerke’s R 2. End-points were mortality and unfavorable outcome (Glasgow Outcome Scale–Extended score 1–4) at 6 month post-injury. We estimated 95% confidence intervals (CIs) with bootstrap resampling to summarize the improvement in prognostic capability. In a meta-analysis of CENTER-TBI and TRACK-TBI, GCS as a linear score had a R 2 of 25% (95% CI 19–31%) for mortality and 33% (4–41%) for unfavorable outcome. Pupillary reactivity as a separate variable improved the R 2 by an absolute value of 6% (4.0–7.7%) and 2% (1.2–3.0%) for mortality and unfavorable outcome, respectively, while comparatively half of this improvement was captured by the GCS–P score (3% [2.1–3.3%], 1% [1–1.7%], respectively). GCS–P showed a stronger association with 6-month outcome after TBI than GCS alone and provides a single integrated score. However, this comes at a loss of clinical and prognostic information compared with GCS+P. For prognostic models, inclusion of GCS and pupillary reactivity as separate factors may be preferable to using a GCS–P summary score.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".