Do not resuscitate orders and aging: Impact of multimorbidity on the decision-making process
Bibliographic record
Abstract
BACKGROUND: The "Do Not Resuscitate" orders (DNR) are defined as advance medical directives to withhold cardiopulmonary resuscitation during cardiac arrest. Age-related multimorbidity may influence the DNR decision-making process. Our objective was to perform a systematic review and meta-analysis of published data examining the relationship between DNR orders and multimorbidity in older patients. METHODS: A systematic Medline and Cochrane literature search limited to human studies published in English and French was conducted on August 2012, with no date limits, using the following Medical Subject Heading terms: "resuscitation orders" OR "do-not-resuscitate" combined with "aged, 80 and over" combined with "comorbidities" OR "chronic diseases". RESULTS: Of the 65 selected studies, 22 met the selection criteria for inclusion in the qualitative analysis. DNR orders were positively associated with multimorbidity in 21 studies (95%). The meta-analysis included 7 studies with a total of 27,707 participants and 5065 DNR orders. It confirmed that multimorbidity were associated with DNR orders (summary OR = 1.25 [95% CI: 1.19-1.33]). The relationship between DNR orders and multimorbidity differed according to the nature of morbidities; the summary OR for DNR orders was 1.15 (95% CI: 1.07-1.23) for cognitive impairment, OR=2.58 (95% CI: 2.08-3.20) for cancer, OR=1.07 (95% CI: 0.92-1.24) for heart diseases (i.e., coronary heart disease or congestive heart failure), and OR=1.97 (95% CI: 1.61-2.40) for stroke. CONCLUSIONS: This systematic review and meta-analysis showed that DNR orders are positively associated with multimorbidity, and especially with three morbidities, which are cognitive impairment, cancer and stroke.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".