P.116 Days at home after traumatic brain injury: moving beyond mortality to evaluate patient-centered outcomes using population health data
Bibliographic record
Abstract
Background: Despite the utility of administrative health data, there remains a lack of patient-centered outcome measures to meaningfully capture morbidity after traumatic brain injury (TBI). We sought to characterize and validate days at home (DAH) as a feasible measure to assess population-level moderate to severe TBI (msTBI) outcomes and health resource utilization. Methods: We utilized linked health administrative data sources to identify adults with msTBI patients presenting to trauma centers in Ontario injured between 2009-2021. DAH at 180 days reflects the total number of days spent alive and at home excluding the days spent institutionalized in acute care, rehabilitation, inpatient mental health settings or post-acute readmissions. Construct and predictive validity were determined; we additionally estimated minimally important difference (MID) in DAH 180days . Results: There were 6340 patients that met inclusion criteria. Median DAH 180days were 70 days (interquartile range 0-144). Increased health resource utilization at baseline, older age, increasing cranial injury severity and major extracranial injuries were significantly associated with fewer DAH 180days . DAH 180days was correlated to DAH counts at 1-3 years. The average MID estimate from anchor-based and distribution-based methods was 18 days. Conclusions: We introduce DAH 180days as a feasible and sufficiently responsive patient-centered outcome measure with construct, predictive and face validity in an msTBI population.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".