Audit of Do-Not-Resuscitate Order Status of Patients in a Homecare and In-Patient Palliative Care Service
Bibliographic record
Abstract
Do-Not-Resuscitate (DNR) orders vary in prevalence among palliative care services. Some hospice programs require patients to have completed DNR forms prior to service admission. It was our intent to investigate the rates of DNR orders in a multi-tiered, multi-service palliative care program and to compare these rates with ideals and goals of staff. We performed an audit, blinded to patient caregiver, of charts for patients on homecare and in-patient services. We then constructed a questionnaire to investigate staff perceptions and goals for DNR status of patients in their care. After reviewing 87 charts, we determined the prevalence of DNR status to be 48%. From the completed questionnaires, staff estimated (mean [95% CI]) that 72% [58, 86] of patients under their care had DNR orders. Also, staff felt that 96% [90, 101] of patients under their care should have DNR orders in place. There was therefore discrepancy between the prevalence, perceived prevalence, and desired prevalence of DNR orders in this palliative care service. Following this investigation, the lead author chaired an open forum with staff to address these discrepancies. Staff felt a new method of tracking DNR status as well as a more structured timeline to address DNR status with patients on service is warranted. A follow-up study to evaluate improvements spawned from this audit would indeed be interesting.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".