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Record W2591350882 · doi:10.1515/dx-2016-0043

Five things to know about diagnostic error

2017· article· en· W2591350882 on OpenAlexaff
Darya Yermak, Peter Cram, Janice L. Kwan

Bibliographic record

VenueDiagnosis · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsSinai Health SystemUniversity Health NetworkUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsMedical diagnosisDiagnostic testNeed to knowDiagnostic accuracyRoot cause analysisMedicineRoot causePsychologyIntensive care medicineComputer scienceEngineeringPathologyForensic engineeringPediatricsOperations managementComputer security

Abstract

fetched live from OpenAlex

Diagnostic error represents an important patient safety issue. Herein, we summarize five important things to know about this topic. 1) At least 1 in 20 adults are affected by diagnostic errors annually. 2) The root causes for diagnostic errors are typically multifactorial. 3) Cognitive errors are found in the majority of cases. 4) Most missed diagnoses involve common conditions. 5) Advancements in policy, education, and health information technologies hold promise for improving diagnostic safety.

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)0.331
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.331
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.026
GPT teacher head0.359
Teacher spread0.334 · 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 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
Published2017
Admission routes1
Has abstractyes

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