Hospital-based specialist palliative care involvement before and during the COVID-19 pandemic: secondary analysis of a regional retrospective decedent cohort study in Ottawa, Canada
Bibliographic record
Abstract
OBJECTIVES: To determine the occurrence and clinicodemographic associations of hospital-based specialist palliative care (SPC) referral before and during the COVID-19 pandemic, timing of completed SPC consultation and comparative prevalence of 'no cardiopulmonary resuscitation (CPR)' orders, and end-of-life medication use, according to SPC involvement. DESIGN: Cross-sectional secondary analysis of a retrospective cohort study with a pre-pandemic (November 2019 to February 2020) group (Pre-COVID, n=170) and two intra-pandemic (March to August 2020) groups, one without (COVID-ve, n=170) and one with COVID-19 infection (COVID+ve, n=85). In the cohort study, Pre-COVID and COVID-ve group decedents were matched 2:1 on age, sex and care service (internal medicine/intensive care unit (ICU)) at death with COVID+ve decedents. In our current secondary analysis, clinicodemographic variables associated with SPC referral were examined in multivariable logistic regression, reporting adjusted ORs (aORs) and 95% CIs. SETTING: One quaternary and two tertiary acute care hospitals. PARTICIPANTS: Decedent cohort with a terminal hospital admission (N=425). MAIN OUTCOME MEASURES: SPC referral (yes/no) and timing of completed SPC consultation before death. Additional outcomes included 'no CPR' status and end-of-life medication prescription and dosing. RESULTS: SPC referral occurred in 70 (41.2%), 71 (41.8%) and 26 (30.6%) of the Pre-COVID, COVID-ve and COVID+ve groups, respectively (p=0.18). The aORs for SPC referral were lower for deaths in ICU (0.07; 95% CI 0.03 to 0.16) and admissions from nursing homes/long-term care (0.45; 95% CI 0.23 to 0.9), and higher for active cancer (2.5; 95% CI 1.39 to 4.39). Recipients of SPC consultation, compared with non-recipients, more frequently had a 'no CPR' order, had it placed earlier and were more frequently prescribed palliative end-of-life medications. CONCLUSIONS: Hospital SPC consultation rates early in the COVID-19 pandemic were largely maintained at pre-pandemic levels. Having active cancer was positively associated with SPC referral, whereas both ICU death and having a nursing home/long-term care pre-admission source were negatively associated with referral.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".