Comparison of outcomes after appendectomy in First Nations and non–First Nations patients in Northern Alberta
Bibliographic record
Abstract
BACKGROUND: Internationally, Indigenous Peoples experience worse surgical outcomes than non-Indigenous patients, but equity of surgical care is less well studied in Canada. This study compares outcomes after appendectomy in First Nations and non-First Nations patients. METHODS: In this population-based study, we reviewed administrative data of patients who underwent appendectomy between Apr. 1, 2004, and Mar. 31, 2017, in Northern Alberta. Demographic variables and characteristics of surgical care for First Nations and non-First Nations patients were collected. We identified adverse outcomes by the presence of predefined administrative codes. We identified variables related to a complex postoperative course (at least 1 of wound dehiscence, surgical site infection, abscess, bowel obstruction, pneumonia, deep vein thrombosis, sepsis, emergency department visit, readmission or death within 30 d after appendectomy) through a logistic regression model, and those related to longer length of stay using a Cox proportional hazards model. RESULTS: < 0.001). After adjustment for age, sex, distance to hospital, socioeconomic deprivation and comorbidities, First Nations status remained independently associated with higher rates of adverse outcomes (odds ratio 1.548, 95% confidence interval [CI] 1.384-1.733) and longer lengths of stay (hazard ratio 0.877, 95% CI 0.832-0.924). CONCLUSION: Although rurality, comorbidities and socioeconomic status contributed to worse outcomes after appendectomy for First Nations patients, First Nations status remained independently associated with worse surgical outcomes. Surgical care, an integral component of health care delivery, must be improved for First Nations patients in order to achieve equitable health care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".