Bibliographic record
Abstract
Observational data from an adult traumatic brain injury (TBI) cohort derived from a Level I Trauma Centre (TC) over an eight-year time horizon were analyzed using descriptive statistics and logistic regression in a series of four studies. The first study aimed to assess differences between younger and older TBI patients in injury presentation, hospital resource utilization and short-term outcomes. Older adults were found to have reduced odds for TBI in the presence of multisystem injury (TBI+), direct hospital transport, and trauma team activation (TTA). Older age was also associated with increased odds for assisted living disposition and hospital readmission. The second study sought to assess the impact of different factors related to older and younger patients and factors related to mortality after TBI. It was found that increasing age was associated with increased odds (OR: odds ratio) mortality, as was the presence of comorbidities, severity of injury and receiving surgery. The third study aimed to assess determinants of hospital length of stay (LOS) and associated costs for acute care medical treatment. It was found that the mean LOS was 6.4 days, with intensive care unit (ICU) admissions and alternate level of care (ALC) designated patients accounting for 23% and 13% of total hospital days. The six most influential determinants of acute care hospital LOS were discharge destination, hospital acquired complications, ICU management, Geriatric Trauma Consultation Service (GTCS) exposure, physician service, and TBI+ diagnosis. The mean acute care hospital cost for patients in the cohort was $20,148 CAN (SD: $32,800). TBI+ presentation accounted for 20% of all hospital bed-days and 23% of all hospital expenditures, despite its representation in only 10% of the entire cohort. The fourth study was designed to measure associations between GTCS and hospitalization outcomes for geriatric TBI patients. GTCS patients were matched to those without GTCS (UC: usual care) using propensity scores. GTCS management was significantly associated with increased rate of in-hospital complications, ICU- and ALC-management and prolonged total LOS Among GTCS survivors, there was significant increased disposition to in-patient rehabilitation (IR) (OR 1.37 CI 1.00-1.88), compared to UC patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".