The Incidence of Physiatry-Relevant Complications in Trauma Patients Admitted to an Urban Canadian Trauma Center
Bibliographic record
Abstract
The objective of this study was to describe the incidence of complications in trauma patients that could be prevented, diagnosed, or managed by a consulting acute care physiatrist. Demographic and complication data were extracted by chart review of adult trauma patients admitted to a Canadian academic trauma center. Subjects were included if they had a diagnosis of traumatic brain injury, spinal cord injury, or multiple injuries resulting in an Injury Severity Score greater than 15. Means and standard deviations were calculated for continuous variables and frequencies for categorical data. Secondary analyses involved using Spearman's ρ and χ analysis to examine relationships between the development of complications and various patient factors. A total of 286 individuals were included. The overall incidence of a physical medicine & rehabilitation-relevant complication was 32.9%. The complications with the highest incidence were pneumonia (15.5%), delirium (14.1%), and urinary tract infection (13.4%). Secondary analyses demonstrated associations between the development of complications with older age, the presence of comorbidities, having both a traumatic brain injury and spinal cord injury, and length of stay. This study demonstrated that trauma patients may experience multiple complications that are of relevance to the consulting physiatrist.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".