Using the “Surprise Question” in Nursing Homes
Bibliographic record
Abstract
BACKGROUND: The "Surprise Question" (SQ) is often used to identify patients who may benefit from a palliative care approach. The time frame of the typical question (a 12-month prognosis) may be unsuitable for identifying residents in nursing homes since it may not be able to differentiate between those who have a more imminent risk of death within a cohort of patients with high care needs. OBJECTIVE: To examine the accuracy and acceptability of 3 versions of the SQ with shortened prognostication time frames (3 months, 6 months, and "the next season") in the nursing home setting. DESIGN: A prospective mixed-methods study. SETTING/PARTICIPANTS: Forty-seven health-care professionals completed the SQ for 313 residents from a nursing home in Ontario, Canada. A chart audit was performed to evaluate the accuracy of their responses. Focus groups and interviews were conducted to examine the participants' perspectives on the utility of the SQ. RESULTS: Of the 301 residents who were included in the analysis, 74 (24.6%) deaths were observed during our follow-up period. The probability of making an accurate prediction was highest when the seasonal SQ was used (66.7%), followed by the 6-month (58.9%) and 3-month (57.1%) versions. Despite its high accuracy, qualitative results suggest the staff felt the seasonal SQ was ambiguous and expressed discomfort with its use. CONCLUSION: The SQ with shortened prognostication periods may be useful in nursing homes and provides a mechanism to facilitate discussions on palliative care. However, a better understanding of palliative care and increasing staff's comfort with prognostication is essential to a palliative care approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".