The impact of traumatic injury in the oil and gas industry
Bibliographic record
Abstract
Introduction The oil and gas industry employs approximately 390,000 people in Canada and these workers are often exposed to substantial workplace risks. Trauma centres treat a significant number of industry-related injuries; however, studies characterizing these traumatic events are lacking. Methods A retrospective study was conducted of workers in the oil and gas industry admitted to major trauma centres in Edmonton, Alberta from January 2009 to December 2014. Patients were identified from Alberta Trauma Registry and Worker's Compensation Board data. Inclusion criteria were: age ≥16 years, trauma occurring in the oil and gas industry, and Injury Severity Score (ISS) ≥ 12 or Modified Abbreviated Injury Scale (MAIS) ≥ 3. Descriptive analysis and cost estimation were performed. Results There were 182 major traumas occurring primarily in young males. Blunt trauma was the primary mechanism (90.1%), and alcohol levels were positive in 4.8% of patients. The overall complication rate was 32.4% with a mortality rate of 6%. The majority of patients were discharged home (64.3%), however a large proportion (29.7%) required further care at another facility post-acute care. The median days missed from work were 85 (IQR 7–214.5). Total cost of injury from the societal perspective ranged from $109 965 to $332 098 USD per person. Conclusion Oil and gas industry trauma has a high economic and societal cost. Strategies to prevent injuries in this field should be undertaken including promotion and support of drug-free programmes given the rate of positive alcohol screening in this population.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".