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Record W2947600391 · doi:10.1148/radiol.2019182206

Publication Bias: Association of Diagnostic Accuracy in Radiology Conference Abstracts with Full-Text Publication

2019· article· en· W2947600391 on OpenAlexaff
Lindsay A. Cherpak, Daniël A. Korevaar, Trevor A. McGrath, Wilfred Dang, Daniel Walker, Jean‐Paul Salameh, Anahita Dehmoobad Sharifabadi, Matthew D. F. McInnes

Bibliographic record

VenueRadiology · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineConfidence intervalLogistic regressionPublication biasConfoundingOdds ratioDiagnostic accuracyYouden's J statisticMEDLINEMedical physicsFamily medicineInternal medicineReceiver operating characteristic

Abstract

fetched live from OpenAlex

Background Recent investigations have identified a faster time to publication for imaging studies with higher diagnostic test accuracy (DTA), but it is unknown whether such studies are more likely to be published. A higher probability of full-text publication for studies with higher DTA could have negative consequences on clinical decision making and patient care. Purpose To evaluate the proportion of imaging diagnostic accuracy studies presented as conference abstracts that reach full-text publication and to identify whether there is an association between diagnostic accuracy and full-text publication in peer-reviewed journals within 5 years after abstract submission. Materials and Methods Diagnostic accuracy research abstracts presented at the Radiological Society of North America (RSNA) Annual Meeting in 2011 and 2012 were evaluated between September 1, 2017, and January 11, 2018. Sensitivity and specificity from the abstracts were used to calculate the Youden index (sensitivity + specificity−1); additional abstract characteristics were extracted. To identify full-text publications within 5 years after abstract submission, PubMed and Google Scholar were searched, and authors were contacted. Logistic regression analysis was used to assess for associations between higher diagnostic accuracy and full-text publication. Results A total of 7970 abstracts were evaluated, and 405 were included. Of these, 288 (71%) reached full-text publication within 5 years after abstract submission. Logistic regression analysis accounting for several confounding variables failed to show an association between reported Youden index in the conference abstract and probability of full-text publication (odds ratio, 1.01; 95% confidence interval: 0.99, 1.02; P = .21). Conclusion More than a quarter of abstracts presented at the RSNA Annual Meeting do not reach full-text publication in peer-reviewed journals. The magnitude of reported diagnostic accuracy was not associated with full-text publication, which is consistent with results of diagnostic accuracy studies in other medical specialties. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Fielding in this issue.

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)0.057
metaresearch head score (Gemma)0.269
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.269
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.005

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 head0.426
GPT teacher head0.442
Teacher spread0.015 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2019
Admission routes1
Has abstractyes

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