Emergency physicians’ experiences managing patients with a suspected cancer diagnosis in Ontario, Canada: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: The emergency department (ED) often serves as a crucial pathway for cancer diagnosis. However, little is known about the management of patients with new suspected cancer diagnoses in the ED. The objective of this study was to explore emergency physicians' experiences in managing patients with a newly suspected cancer diagnosis in the ED. DESIGN: Between January and April 2024, we conducted a qualitative descriptive study. Interviews were conducted by trained research personnel using a semistructured interview guide. Interviews were conducted until thematic saturation was achieved. The interview transcripts were coded and thematic analysis was used to uncover key themes. SETTING AND PARTICIPANTS: Emergency physicians practising in Ontario, Canada. RESULTS: 20 emergency physicians were interviewed. Four themes around the management of patients with new suspected cancer diagnoses in the ED were identified: (1) healthcare system-level factors that impact suspected cancer diagnosis through the ED, (2) institutional and provider-level challenges associated with managing patients with a suspected cancer diagnosis in the ED, (3) patient-level characteristics and experiences of receiving a cancer diagnosis in the ED and (4) the need for care coordination for patients with suspected cancer in the ED. CONCLUSIONS: Physicians experienced several unique challenges in managing patients with a suspected cancer diagnosis in the ED. Overall, the findings of this study suggest these challenges often make the ED a difficult environment in which to deliver a suspected cancer diagnosis.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".