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Record W3082115751 · doi:10.1177/0194599820954138

Shared Decision Making for Surgical Care in the Era of COVID‐19

2020· article· en· W3082115751 on OpenAlexaff
David Forner, Christopher W. Noel, Ryan Densmore, David P. Goldstein, Martin Corsten, Arwen H. Pieterse, Andrew G. Shuman, Paul Hong, Valeria E. Rac

Bibliographic record

VenueOtolaryngology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsDiabetes CanadaTed Rogers Centre for Heart ResearchDalhousie UniversityUniversity of TorontoToronto General HospitalUniversity Health NetworkInstitute for Work & Health
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Consistency (knowledge bases)PerioperativeMedicineHealth careProcess (computing)Medical emergencyIntensive care medicineRisk analysis (engineering)BusinessDiseaseInfectious disease (medical specialty)Computer scienceSurgeryEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 has upended surgical practice. In an effort to preserve resources, mitigate risk, and maintain health system capacity, nonurgent surgeries have been deferred in many jurisdictions, with urgent procedures facing increasing wait times and unpredictability given potential future surges. Shared decision making, a process that integrates patient values and preferences with the scientific expertise of clinicians, may be of particular benefit during these unprecedented times. Aligning patient choices with their values, reducing unnecessary health care use, and promoting consistency between providers are now more critical than ever before. We review important aspects of shared decision making and provide guidance for its perioperative application during the coronavirus disease 2019 pandemic.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 science0.0010.000
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 head0.203
GPT teacher head0.459
Teacher spread0.256 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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