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Record W3023363969 · doi:10.1503/cjs.003218

Decisional conflict in surgical patients: Should surgeons care?

2019· article· en· W3023363969 on OpenAlexafffundvenue
Mélissa Roy, Christine B. Novak, David R. Urbach, Steven J. McCabe, Herbert P. von Schroeder, Karen Okrainec

Bibliographic record

VenueCanadian Journal of Surgery · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsRegretMedicineScale (ratio)Quality (philosophy)Action (physics)Nursing

Abstract

fetched live from OpenAlex

<h3>Summary</h3> Decisional conflict represents a state of uncertainty regarding an action one must take. It is a concept inherent to shared decision-making and can help promote high-quality and patient-centred decisions in surgical care, leading to better outcomes. Specific elements may cause more uncertainty or decisional conflict for patients: lack of knowledge about risks and benefits, poorly defined personal values about the importance of those risks and benefits, perception of a lack of support, unpredictable outcomes, or the impression that an inadequate decision has been made. Decisional conflict can be measured in the surgical setting using the 16-item validated patient-reported Decisional Conflict Scale (DCS). Better understanding of the reasons behind high decisional conflict can help surgeons support high-quality decisions and lead to more satisfactory outcomes and less decisional regret.

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)0.006
metaresearch head score (Gemma)0.078
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.078
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0120.002

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 head0.407
Teacher spread0.280 · 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

Citations7
Published2019
Admission routes3
Has abstractyes

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