CPR or DNR? End-of-life decision making on a family practice teaching ward.
Bibliographic record
Abstract
OBJECTIVE: To determine the proportion of patients on a family practice ward who had "code status" orders and end-of-life discussions documented on their charts in the first week of admission. To examine the correlation between a tool predicting the likelihood of benefit from cardiopulmonary resuscitation (CPR) and actual end-of-life decisions made by family physicians and their patients. DESIGN: Cross-sectional descriptive study using a retrospective chart review. SETTING: A 14-bed teaching ward where family physicians admit and manage their own patients in an urban tertiary care teaching hospital. PARTICIPANTS: Patients admitted to the ward for 7 or more days between December 1, 1995, and August 31, 1996. MAIN OUTCOME MEASURES: Frequency of documented "do not resuscitate" (DNR) or "full code" orders and documented end-of-life discussions. Prognosis-after-resuscitation (PAR) score. RESULTS: In the 103 charts reviewed, code status orders were entered within 7 days for 60 patients (58%); 31 were DNR, and 29 were full code. Discussion of code status was documented in 25% of charts. The PAR score for 40% of patients was higher than 5, indicating they were unlikely to survive to discharge from hospital should they require CPR. There was a significant association between PAR scores done retrospectively and actual code status decisions made by attending family physicians (P < .005). CONCLUSIONS: End-of-life discussions and decisions were not fully documented in patients' charts, even though patients were being cared for in hospital by their family physicians. A PAR score obtained during the first week of admission could assist physicians in discussing end-of-life orders with their patients.
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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 | 0.005 |
| 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) | 0.003 | 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".