Resource Use in the Last Three Months of Life by Lung Cancer Patients in Southern Ontario
Bibliographic record
Abstract
Background: End-of-life cancer care involves multidisciplinary teams working in various settings. Evaluating the quality of care and the feedback from such processes is an important aspect of health care quality improvement. Our retrospective cohort study reviewed health care use by lung cancer patients at end of life, their reasons for visiting the emergency department (ED), and feedback from regional health care professionals. Methods: We assessed 162 Ontario patients with small-cell and relapsed or advanced non-small-cell lung cancer. Demographics, disease characteristics, and resource use were collected, and the consenting caregivers for patients with ed visits were interviewed. Study results were disseminated, and feedback about barriers to care was sought. Results: Median patient age was 69 years; 73% of the group had non-small-cell lung cancer; and 39% and 69% had received chemotherapy and radiation therapy respectively. Median overall survival was 5.6 months. In the last 3 months of life, 93% of the study patients had visited an oncologist, 67% had telephoned their oncology team, 86% had received homecare, and 73% had visited the ed. Death occurred for 55% of the patients in hospital; 23%, at home; and 22%, in hospice. Goals of care had been documented for 68% of the patients. Homecare for longer than 3 months was associated with fewer ed visits (80.3% vs. 62.1%, p = 0.022). Key themes from stakeholders included the need for more resources and for effective communication between care teams. Conclusions: Use of acute-care services and rates of death in an acute-care facility are both high for lung cancer patients approaching end of life. In our study, interprofessional and patient–provider communication, earlier connection to homecare services, and improved access to community care were highlighted as having the potential to lower the need for acute-care resources.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".