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Record W2418006695 · doi:10.1177/082585971102700104

Specialist Physician Approaches to Discussing Cardiopulmonary Resuscitation for Frail Older Adults: A Qualitative Study

2011· article· en· W2418006695 on OpenAlexaff
Laurie Mallery, Ruth E. Hubbard, Paige Moorhouse, Katalin Koller, Eamonn Eeles

Bibliographic record

VenueJournal of Palliative Care · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsDalhousie UniversityQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsCardiopulmonary resuscitationAdvance care planningAutonomyMedicineLife expectancyQualitative researchResuscitationHealth careMEDLINEFamily medicineNursingPalliative carePsychologyEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the impact and importance of end-of-life discussions, little is known about how physicians discuss cardiopulmonary resuscitation (CPR) with patients and their families. The necessary components for successful communication about CPR are poorly understood and an established framework to structure these conversations is lacking. Here, we were motivated to understand how physicians approach resuscitation planning with families when older patients have limited life expectancy and a high burden of illness. METHOD: Qualitative analysis was conducted of semi-structured interviews of 28 physicians of varying medical sub-specialties in a tertiary care hospital. RESULTS: Most physicians explored the surrogates' goals and values, but few provided explicit information about the patients' overall health status or expected long-term health outcome related to CPR and underlying illnesses. CONCLUSION: There is considerable heterogeneity in physicians' approaches to CPR discussions. The principle of autonomy is dominant with less emphasis on providing adequate information for effective decision-making.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.337
GPT teacher head0.442
Teacher spread0.105 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations10
Published2011
Admission routes1
Has abstractyes

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