Traumatic Brain Injury in the Workplace
Bibliographic record
Abstract
OBJECTIVES: Work-related traumatic brain injuries (TBIs) are not well documented in the literature. Published studies mostly rely on worker databases that fail to provide clinically relevant information. Our objective is to describe the characteristics of hospitalized patients and their work-related TBI. METHODS: We used the Québec provincial trauma and TBI program databases to identify all patients with a diagnosis of work-related TBI admitted to the Montreal General Hospital, a level 1 trauma center, between 2000 and 2014. Data from their medical records were extracted using a predetermined information sheet. Simple descriptive statistics (means and percentages) were used to summarize the data. RESULTS: A total of 285 cases were analyzed. Workplace TBI patients were middle-aged (mean, 43.62 years), overwhelmingly male (male:female 18:1), mostly healthy, and had completed a high school level education. Most workers were from the construction industry; falling was the most common mechanism of injury. The majority of patients (76.8%) presented with a mild TBI; only a minority (14%) required neurosurgery. The most common finding on computed tomography was skull fracture. The median length of hospitalization was 7 days, after which most patients were discharged directly home. A total of 8.1% died of their injuries. CONCLUSIONS: Our study found that most hospitalized victims of work-related TBI had mild injury; however, some required neurosurgical intervention and a non-negligible proportion died of their injury. Improving fall prevention, accurately document helmet use and increasing the safety practice in the construction industry may help decrease work-related TBI burden.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".