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Record W4380730823 · doi:10.1111/hdi.13103

Discrepancy in responses to the surprise question between hemodialysis nurses and physicians, with focus on patient clinical characteristics: A comparative study

2023· article· en· W4380730823 on OpenAlexvenueno aff
Jeanette M. Wallin, Stefan H. Jacobson, Lena Axelsson, Jenny Lindberg, Carina Persson, Jenny Stenberg, Agneta Wennman‐Larsen

Bibliographic record

VenueHemodialysis International · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersStiftelsen Silviahemmet
KeywordsMedicineHemodialysisSurpriseLogistic regressionFamily medicineObservational studyInternal medicinePsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.126
GPT teacher head0.460
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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