Provider Perceptions Regarding Cardiopulmonary Resuscitation in Surgical Patients With Frailty
Bibliographic record
Abstract
OBJECTIVE: To characterize the perceptions of surgeons, anesthesiologists, and geriatricians regarding perioperative cardiopulmonary resuscitation (CPR) in surgical patients with frailty. BACKGROUND: The population of patients undergoing surgery is growing older and more frail. Despite a growing focus on goal-concordant care, frailty assessment, and debate regarding the appropriateness of CPR in patients with frailty, providers' views regarding frailty and perioperative CPR are unknown. METHODS: We performed qualitative thematic analysis of transcripts from semistructured interviews of anesthesiologists (8), surgeons (10), and geriatricians (9) who care for high-risk surgical patients at 2 academic medical centers in Boston, MA. The interview guide elicited clinicians' understanding of frailty, approach to decision-making regarding perioperative CPR, and perceptions of perioperative CPR in frail surgical patients. RESULTS: We identified 5 themes: (1) perceptions of perioperative CPR in patients with frailty vary by provider specialty, (2) judgments regarding the appropriateness of CPR in surgical patients with frailty are typically multifactorial and include patient goals, age, comorbidities, and arrest etiology, (3) resuscitation in patients with frailty is sometimes associated with moral distress, (4) biases, such as ableism and ageism, may skew clinicians' perceptions of the appropriateness of perioperative CPR in patients with frailty, and (5) evidence to guide risk stratification for patients with frailty undergoing perioperative CPR is inadequate. CONCLUSIONS: Anesthesiologists, surgeons, and geriatricians offer different accounts of frailty's relevance to judgments regarding CPR in surgical patients. Divergent views regarding frailty and perioperative CPR may impede efforts to deliver goal-concordant care and suggest a need for research to inform risk stratification, predict patient-centered outcomes, and understand the role of potential biases, such as ageism and ableism.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".