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Record W2038410295 · doi:10.1177/0272989x14564433

The Neglected Topic

2015· article· en· W2038410295 on OpenAlexfundaboutno aff
J. S. Blumenthal‐Barby, Emily Robinson, Scott B. Cantor, Aanand D. Naik, Heidi V. Russell, Robert J. Volk

Bibliographic record

VenueMedical Decision Making · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersOttawa Hospital Research Institute
KeywordsDecision aidsDecision analysisOptimal decisionDecision support systemCost–benefit analysisPresentation (obstetrics)Cost effectivenessMedicineActuarial scienceComputer scienceOperations managementRisk analysis (engineering)Decision treeBusinessEconomicsSurgeryAlternative medicineArtificial intelligencePathology

Abstract

fetched live from OpenAlex

Costs are an important component of patients' decision making, but a comparatively underemphasized aspect of formal shared decision making. We hypothesized that decision aids also avoid discussion of costs, despite their being tools designed to facilitate shared decision making about patient-centered outcomes. We sought to define the frequency of cost-related information and identify the common modes of presenting cost and cost-related information in the 290 decision aids catalogued in the Ottawa Hospital Research Institute's Decision Aid Library Inventory (DALI) system. We found that 56% (n = 161) of the decision aids mentioned cost in some way, but only 13% (n = 37) gave a specific price or range of prices. We identified 9 different ways in which cost was mentioned. The most common approach was as a "pro" of one of the treatment options (e.g., "you avoid the cost of medication"). Of the 37 decision aids that gave specific prices or ranges of prices for treatment options, only 2 were about surgery decisions despite the fact that surgery decision aids were the most common. Our findings suggest that presentation of cost information in decision aids is highly variable. Evidence-based guidelines should be developed by the International Patient Decision Aid Standards (IPDAS) Collaboration.

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)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.334
GPT teacher head0.512
Teacher spread0.178 · 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.

Study designNot applicable
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

Citations22
Published2015
Admission routes2
Has abstractyes

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