End-of-Life Health Resource Utilization for Limited English-Proficient Patients With Advanced Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Limited English-proficient (LEP) patients with non–small-cell lung cancer (NSCLC) may receive less palliative care services and more likely to receive aggressive end-of-life (EoL) care. Goals of this retrospective cohort study are to compare access to community palliative home care (CPHC), do not resuscitate (DNR) form completion, place of death, and health resource utilization at EoL between English-proficient (EP) and LEP patients with NSCLC in Vancouver, Canada. METHODS: All patients with advanced NSCLC referred in 2016 and received medical care were included. Patients were classified as LEP if seen with a medical interpreter. Descriptive statistics and univariate and multivariate analyses were used to compare the outcomes between the two groups. RESULTS: One hundred eighty-six patients were referred, 66% EP. Rates of CPHC referral and DNR form completion were the same for both groups (84% and 92%, P = 1.00). LEP patients received earlier access to CPHC (15 v 10 weeks before death, P = .039). Rates of ER visits within 6 months and 30 days of death were 0.89 for EP patients and 0.7 for LEP patients, P = .374, and 0.1 for EP patients and 0.13 for LEP patients, P = .244. Hospitalization rates within 6 months and 30 days of death were 1.4 for EP patients and 1.59 for LEP patients, P = .640, and 0.67 for EP patients and 0.81 for LEP patients, P = .091. EP patients were more likely to have a home death (26% v 14%), whereas LEP patients died in acute care (23% v 14%) or a tertiary palliative care unit (24% v 19%). This was not statistically significant ( P = .335). LEP patients had better median overall survival (8.5 v 5.4 months, P < .001), but when controlled by age, mutation, and EP status, only receipt of palliative-intent systemic therapy was statistically significant. CONCLUSION: EP and LEP patients with NSCLC have similar referral rates to CPHC, DNR form completion, and EoL health resource utilization. The measured EoL variables did not demonstrate significant disparities between EP and LEP patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".