Discrepancy in responses to the surprise question between hemodialysis nurses and physicians, with focus on patient clinical characteristics: A comparative study
Bibliographic record
Abstract
INTRODUCTION: The surprise question (SQ) "Would I be surprised if this patient died within the next xx months" can be used by different professions to foresee the need of serious illness conversations in patients approaching end of life. However, little is known about the different perspectives of nurses and physicians in responses to the SQ and factors influencing their appraisals. The aim was to explore nurses' and physicians' responses to the SQ regarding patients on hemodialysis, and to investigate how these answers were associated with patient clinical characteristics. METHODS: This comparative cross-sectional study included 361 patients for whom 112 nurses and 15 physicians responded to the SQ regarding 6 and 12 months. Patient characteristics, performance status, and comorbidities were obtained. Cohen's kappa was used to analyze the interrater agreement between nurses and physicians in their responses to the SQ and multivariable logistic regression was applied to reveal the independent association to patient clinical characteristics. FINDINGS: Proportions of nurses and physicians responding to the SQ with "no, not surprised" was similar regarding 6 and 12 months. However, there was a substantial difference concerning which specific patient the nurses and physicians responded "no, not surprised", within 6 (κ = 0.366, p < 0.001, 95% CI = 0.288-0.474) and 12 months (κ = 0.379, p < 0.001, 95% CI = 0.281-0.477). There were also differences in the patient clinical characteristics associated with nurses' and physicians' responses to the SQ. DISCUSSION: Nurses and physicians have different perspectives in their appraisal when responding to the SQ for patients on hemodialysis. This may reinforce the need for communication and discussion between nurses and physicians to identify the need of serious illness conversations in patients approaching the end of life, in order to adapt hemodialysis care to patient preferences and needs.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".