Patterns of emergency department visits preceding colorectal cancer diagnosis: a population-based study
Bibliographic record
Abstract
Aim: To assess the patterns of emergency department (ED) visits in the 3 months preceding a diagnosis of colorectal cancer (CRC) in a real-world, population-based context. Materials & methods: Linked provincial registries in Alberta, Canada, were accessed and patients with CRC diagnosed between 2004 and 2018 were identified. The National Ambulatory Care reporting system was used to identify patients who visited an ED within 3 months of a diagnosis of CRC. Multivariable logistic regression analysis was used to identify factors associated with any ED visits as well as frequent (≥3) ED visits. Results: A total of 25,310 patients with CRC were included in the current study. These include 10,126 patients (40%) who had at least one visit to the ED in the 3 months before a diagnosis of CRC diagnosis and 613 patients (2.4%) who visited the ED multiple (≥3) times. The following factors were associated with any visit to an ED: older age (odds ratio [OR]: 1.010; 95% CI: 1.008–1.012), female gender (OR: 1.23; 95% CI: 1.16–1.30), higher comorbidity index (OR: 1.38; 95% CI: 1.35–1.41), metastatic disease (OR: 2.37; 95% CI: 2.23–2.53), proximal tumors (OR: 1.59; 95% CI: 1.50–1.68) and North zone (OR vs south zone: 1.75; 95% CI: 1.55–1.98). Conclusion: It is not uncommon for CRC patients to visit the ED at least once in the 3 months prior to having such a diagnosis. Factors associated with frequent pre diagnosis emergency visits included female gender, higher burden of comorbid disease, advanced stage, proximal tumors and living in the North zone of Alberta (where there is limited access to specialist 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".