Bibliographic record
Abstract
Background: When patients' goals of care have shifted toward comfort, treatment should focus on alleviating symptoms rather than prolonging life at the expense of comfort. Objective: To determine whether the number of noncomfort medications is associated with deprescribing in patients seen by a home-visiting palliative care physician. Design: Single-centre retrospective chart review of patients cared for in the home setting by a specialty palliative care program to determine factors associated with deprescribing. All medications on initial consult were classified as comfort, possibly for comfort, and definitely not for comfort (DNC). Patients were stratified depending on whether intentional deprescribing occurred. Data were analyzed for associations between deprescribing and other variables: number and proportion of DNC medications, diagnosis, palliative performance scale (PPS), number of encounters, code status, preferred place of death, and time to death. Setting: Study population included 80 patients followed by specialist home-visiting palliative physicians in a tertiary center. Inclusion criteria were adult patients with PPS ≤60%, initially seen by a home-visiting palliative physician between 2016 and 2018 and followed for at least 60 days or until death. Results: Deprescribing occurred in 44% of study patients within 60 days. Median number of DNC medications was 3 in the deprescribed group and 0 in the nondeprescribed group ( p < 0.001). Proportion of DNC medications was 29% in the deprescribed group and 15% in the nondeprescribed group ( p < 0.01). Conclusions: Deprescribing is associated with an increased number and proportion of DNC medications at the time of initial in-home palliative assessment. Deprescribing rates varied greatly between different home-visiting palliative providers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".