P.176 Latent home time trajectories for isolated moderate to severe traumatic brain injury survivors: a data-driven method identifies distinct outcome phenotypes
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) patients exhibit variable post-injury recovery trajectories. Days at Home (DAH) is a patient-centered measure that captures healthcare transitions and offers a more nuanced understanding of recovery. Here, we use DAH to characterize longterm recovery trajectories for moderate to severe TBI (msTBI) survivors. Methods: This multicenter retrospective cohort study utilized population health data from Ontario to identify adults sustaining isolated msTBI hospitalized between 2009-2021. DAH were calculated in distinct 30-day intervals from index admission to 3 years post-injury; latent class mixed modeling identified unique recovery trajectories and trajectory attributes were quantified. Results: There were 2,510 patients eligible for latent class analysis. Four DAH trajectories were identified: early recovery (69.9%), intermediate recovery (11.4%), late recovery (2.9%), and poor recovery (15.8%). Patients in the poor recovery group were older, more frail, and had lower admission GCS scores, while those in early recovery exhibited lower acute care needs. Intermediate and late recovery groups exhibited protracted transitions home, with near-complete reintegration by 24 months. A prediction model distinguished unfavorable trajectories with good accuracy (C-index=0.824). Conclusions: Despite high initial institutional care requirements, 85% of patients reintegrated into the community within three years of msTBI. These findings shed light on post-injury care requirements for brain-injured patients.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".