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Record W2075844793 · doi:10.1161/strokeaha.114.005462

Decision Making in Acute Stroke Care

2014· review· en· W2075844793 on OpenAlexafffund
Gustavo Saposnik, S. Claiborne Johnston

Bibliographic record

VenueStroke · 2014
Typereview
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsSt. Michael's Hospital
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineStroke (engine)NeurologyAcute strokeUnit (ring theory)Health careFamily medicineMedical emergencyEmergency departmentPsychiatryPsychologyLaw

Abstract

fetched live from OpenAlex

M aking decisions in medical care is a difficult task, involv- ing a variety of cognitive processes.Decision making is defined as the process of examining possibilities, risks, uncertainties, and options, comparing them, and choosing a course of action.1,2 Decisions based on erroneous assessments may result in incorrect patient and family expectations, and potentially inappropriate advice, treatment, or discharge planning (eg, longer length of hospitalization, long-term placement, and wasted resources).Rapid and accurate decision making is critical to stroke care, for which several factors have proven effect on outcomes.[3][4][5][6][7] In brief, there are patient-level, hospital-level, and provider-level characteristics that directly affect stroke outcomes (Figure 1).7,8 There is limited information on how clinicians make decisions and predict outcomes.Some clinicians apply the knowledge they have acquired from previous experience, others use information available at the time of the assessment, and others use risk score tools or a combination of the above.A better understanding of the decision-making process when treating patients with an acute ischemic stroke could increase clinician awareness of unconscious biases and sources of error and allow the implementation of cognitive shortcuts to make accurate decisions when facing difficult clinical scenarios.Herein, we review different principles and disproven myths revealed by neuroeconomics and neuromarketing and lessons learned from professional poker players, to assist clinicians in making prompt, rational, and accurate decisions in acute stroke care. How We Make Decisions?Neuroeconomics is the science that studies the principles of how we make decisions.9,10 Neuromarketing is the science that studies consumers' sensorimotor, cognitive, and affective response to marketing stimuli.11 The neuroscience of decision making is based on statistical methods and mathematical approaches, such as game theory, to predict and to model how people make their own choices.12 Essentially, there are 2 major types of decisions: (1) programmed: a decision that is repetitive, automatic, and routine and can be made using a systematic approach (eg, most tissue-type plasminogen activator [tPA] decisions in acute stroke) and ( 2) nonprogrammed: a decision that is unique, individual, or requires a thoughtful analysis (eg, assessing potential risk and benefits in a particular context).Other authors have identified similar categories.For example, decisions may rely on either system 1 (intuitive, unconscious, effortless, fast, and emotional) or system 2 (deliberate, conscious reasoning, slow, and effortful), sometimes referred to as Plato's 2 horses and a chariot.13 Practical marketing concepts highlight the underlying steps involved in the decision-making process (called 6 Cs of decision making; Figure I in the onlineonly Data Supplement).In summary, we spend our lives making decisions and helping our patients decide by facilitating information, gathering, and providing counseling. Understanding Risks: the Amplifying Effect of Aging and Comorbid ConditionsThe worldwide population is aging.14 Data from the United Nations suggest that the number of older patients has tripled in the past 50 years and will triple during the next 50 years (United Nations; http://www.un.org/esa/population/publications/ worldageing19502050/index.htm; accessed February 23, 2014).Given the increased prevalence of several stroke risk factors with age (eg, hypertension, atrial fibrillation, and cardiac failure), the longer life expectancy and aging of the population, clinicians will likely face more older patients with stroke and a higher prevalence of a combination of comorbid conditions affecting stroke outcomes.14,15 As illustrated in Figure 2, our patients carry a medical backpack containing risk factors and comorbid conditions, which becomes heavier with aging.Some studies suggest that higher risk of death and disability is associated with higher prevalence of comorbid conditions.This phenomenon has been called the amplifying effect of age and comorbidities (Figure 2).4,16,17 Because some comorbid conditions (ie, cardiac failure and atrial fibrillation) are independent predictors of stroke outcomes, an individual risk assessment is needed.4,18

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)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.427
Teacher spread0.385 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2014
Admission routes2
Has abstractno

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