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Participation of haemato-oncological patients in medical decision making and their confidence in decisions

2010· article· en· W2110417995 on OpenAlexaff
Jochen Ernst, Gregor Weißflog, Elmar Brähler, Dietger Niederwieser, Annett Körner, Christina Schröder

Bibliographic record

VenueEuropean Journal of Cancer Care · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineMedical decision makingClinical decision makingConfidence intervalFamily medicineDecision-makingPerceptionPatient participationMedical careMEDLINEInternal medicinePsychology

Abstract

fetched live from OpenAlex

ERNST J., WEISSFLOG, G., BRÄHLER E., NIEDERWIESER D., KÖRNER A. & SCHRÖDER C. (2010) European Journal of Cancer CareParticipation of haemato-oncological patients in medical decision making and their confidence in decisions Increasingly more clinical care and research acknowledge the patients' interest in participating in medical decision making. However, for haematological patients, there are as yet only modest findings. The current study explores patients' perceptions of their role in the medical decision-making process in a sample of 117 haematological patients. The majority of patients surveyed (63.9%) took a passive role in the medical decision-making process, which is a significantly greater proportion compared with individuals suffering from solid cancers. Despite passive majority, most of the participants reported a positive evaluation of the decision-making process. Importantly, patients' evaluations were significantly more negative either if patients were treated as inpatients (vs. outpatients), or if they experienced no control over the decision (vs. collaboration with the doctor, or deciding autonomously). The results and limitations of the study are discussed.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.180
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.159
GPT teacher head0.476
Teacher spread0.317 · 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 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

Citations16
Published2010
Admission routes1
Has abstractyes

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