Differences in End-of-Life Care between COVID-19 Inpatient Decedents with English Proficiency and Limited English Proficiency
Bibliographic record
Abstract
Background: Patients with limited English proficiency (LEP) experience lower quality end-of-life (EOL) care. This inequity may have been exacerbated during the COVID-19 pandemic. Objective: Compare health care utilization, EOL, and palliative care outcomes between COVID-19 decedents with and without LEP during the pandemic's first wave in Massachusetts. Methods: Retrospective cohort study of adult inpatients who died from COVID-19 between February 18, 2020 and May 18, 2020 at two academic and four community hospitals within a greater Boston health care system. We performed multivariable regression adjusting for patient sociodemographic variables and hospital characteristics. Primary outcome was place of death (intensive care unit [ICU] vs. non-ICU). Secondary outcomes included hospital and ICU length of stay and time to initial palliative care consultation. Results: Among 337 patients, 89 (26.4%) had LEP and 248 (73.6%) were English proficient. Patients with LEP were less often white (24 [27.0%] vs. 193 [77.8%]; p < 0.001); were more often Hispanic or Latinx (40 [45.0%] vs. 13 [5.2%]; p < 0.001); and less often had a medical order for life-sustaining treatment (MOLST) on admission (15 [16.9%] vs. 120 [48.4%]; p < 0.001) versus patients with English proficiency. In the multivariable analyses, LEP was not independently associated with ICU death, ICU length of stay, or time to palliative care consultation, but was independently associated with increased hospital length of stay (mean difference 4.12 days; 95% CI, 1.72–6.53; p < 0.001). Conclusions: Inpatient COVID-19 decedents with LEP were not at increased risk of an ICU death, but were associated with an increased hospital length of stay versus inpatient COVID-19 decedents with English proficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".