Comparison of the Management and Short-Term Outcomes between Patients with Advanced Cancer and Other End-of-Life Conditions Presenting to Two Canadian Emergency Departments
Bibliographic record
Abstract
Background: An increasing number of patients with end-of-life (EOL) conditions, particularly those with advanced cancer, are presenting to the emergency department (ED). Objectives: To assess the characteristics, management and short-term outcomes of ED patients with advanced cancer compared to patients with other EOL conditions. Methodology/Design: A secondary analysis of a prospective cohort study. Setting/Participants: Volunteer emergency physicians in two Canadian EDs identified presentations for advanced cancer and other EOL conditions with the aid of a modified screening tool March–August 2018. Results: Among the 663 presentations by patients with EOL conditions, 272 (41%) presented with advanced cancer. The majority of presentations for advanced cancer (81%) or other EOL conditions (77%) were by patients with unmet palliative care (PC) needs. Patients with advanced cancer were significantly less likely to have active goals of care (GOC) documented on their charts (53% vs. 75%; p < 0.001). While no significant differences were found between the groups, the majority of presentations involved imaging, investigations, consultations, and hospitalization. Presentations for advanced cancer were more likely to receive a postdischarge referral (38% vs. 23%; p < 0.001). Referrals to PC consultations or postdischarge referrals were infrequent. Regression analysis found that patients with advanced cancer were associated with shorter length of stay (LOS). Conclusions: The majority of presentations for advanced cancer or other EOL conditions involved significant resource use. Patients with cancer experienced shorter LOS; however, had less documentation of GOC and gaps in referrals to PC services were identified. Interventions should be explored to promote early GOC discussions and PC referrals in this patient group.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".