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Record W2625102844 · doi:10.1515/dx-2017-0019

Assigning responsibility to close the loop on radiology test results

2017· article· en· W2625102844 on OpenAlexaff
Janice L. Kwan, Hardeep Singh

Bibliographic record

VenueDiagnosis · 2017
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsUniversity of TorontoMount Sinai Hospital
FundersNational Cancer InstituteAgency for Healthcare Research and QualityU.S. Department of Veterans Affairs
KeywordsHarmTest (biology)Context (archaeology)Action (physics)Medical diagnosisSimple (philosophy)Ideal (ethics)Process (computing)Computer sciencePsychologyMedicinePolitical scienceEpistemologySocial psychologyLawRadiologyHistoryBiology

Abstract

fetched live from OpenAlex

Failure to follow-up on test results represents a serious breakdown point in the diagnostic process which can lead to missed or delayed diagnoses and patient harm. Amidst discussions to ensure fail-safe test result follow-up, an important, yet under-discussed question emerges: how do we determine who is ultimately responsible for initiating follow-up action on the tests that are ordered? This seemingly simple question belies its true complexity. Although many of these complexities are also applicable to other diagnostic specialities, the field of medical imaging provides an ideal context to discuss the challenges of attributing responsibility of test result follow-up. In this review, we summarize several key concepts and challenges in the context of critical results, wet reads, and incidental findings to stimulate further discussion on responsibility issues in radiology. These discussions could help establish reliable closed-loop communication to ensure that every test result is sent, received, acknowledged and acted upon without failure.

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)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.034
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 science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.393
Teacher spread0.326 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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