Abstract 360: Evaluating the Challenges of Functional Outcomes in Traumatic Brain Injury Research: Timing of Follow-Up, Prognostic Models, and Missing Data
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) is common and debilitating. Randomized trials of interventions for TBI usually assess effectiveness by using long-term functional neurological outcomes but this is costly and difficult. If patient characteristics available at hospital discharge are predictive of 6-month functional outcome, then shorter-term outcomes may be adequate for use in future clinical trials. We evaluated models to predict long-term outcomes after TBI from short-term functional measures and easily obtainable demographic and injury characteristics as covariates, using data from a previously published randomized clinical trial. Methods: The Hypertonic Saline TBI trial of the Resuscitation Outcomes Consortium (ROC) enrolled 1282 TBI patients but had 15% missing data for the primary outcome of 6-month Glasgow Outcome Score Extended (GOSE). We evaluated patterns of missing data, whether functional outcome obtained earlier than six months would adequately reflect 6-month GOSE, and three prognostic models that predict 6-month severe disability (GOSE ≤ 4) via logistic regression using covariates and outcomes at discharge. Results: Patients with missing 6-month GOSE had less severe injuries, higher neurological function at discharge (GOSE), and shorter hospital stays than patients whose GOSE was obtained. Of 1066 (83%) patients with available discharge and 6-month outcomes, 71.2% of patients had the same functional status (severe disability/death vs. moderate /no disability) after 6 months as at discharge, 28% had an improved functional status, and 1% had worsened. Performance was excellent (AUC between 0.88 and 0.91) for all three prognostic models and calibration adequate for two models (p-values 0.22 and 0.85). Conclusion: Missing data was more common in healthier patients suggesting an ascertainment bias if the missing data were ignored during analysis. Shorter duration follow-up appears inadequate in representing long-term functional neurological outcome following TBI; however, all three prognostic models were highly predictive of long-term outcome. Our results support the more widespread use of multiple imputation of the standard 6-month GOSE when the primary outcome cannot be obtained through other means.
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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 | 0.324 | 0.470 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".