MétaCan
Menu
Back to cohort
Record W1997162001 · doi:10.1097/ruq.0b013e3182814d9b

ACR Appropriateness Criteria® Clinically Suspected Adnexal Mass

2013· article· en· W1997162001 on OpenAlexaff
Robert D. Harris, Marcia C. Javitt, Phyllis Glanc, Douglas L. Brown, Theodore J. Dubinsky, Mukesh G. Harisinghani, Nadia J. Khati, Young Bae Kim, Donald G. Mitchell, Pari V. Pandharipande, Harpreet K. Pannu, Ann E. Podrasky, Henry D. Royal, Thomas Shipp, Cary Siegel, Lynn L. Simpson, Darci J. Wall, Jade J. Wong-You–Cheong, Carolyn M. Zelop

Bibliographic record

VenueUltrasound Quarterly · 2013
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRadiologyGuidelineAdnexal massMedical physicsPathology

Abstract

fetched live from OpenAlex

Adnexal masses are a common problem clinically and imaging-wise, and transvaginal US (TVUS) is the first-line imaging modality for assessing them in the vast majority of patients. The findings of US, however, should be correlated with the history and laboratory tests, as well as any patient symptoms. Simple cysts are uniformly benign, and most warrant no further interrogation or treatment. Complex cysts carry more significant implications, and usually engender serial ultrasound(s), with a minority of cases warranting a pelvic MRI.Morphological analysis of adnexal masses with gray-scale US can help narrow the differential diagnosis. Spectral Doppler analysis has not proven useful in most well-performed studies. However, the use of color Doppler sonography adds significant contributions to differentiating between benign and malignant masses and is recommended in all cases of complex masses. Malignant masses generally demonstrate neovascularity, with abnormal branching vessel morphology. Optimal sonographic evaluation is achieved by using a combination of gray-scale morphologic assessment and color or power Doppler imaging to detect flow within any solid areas.The ACR Appropriateness Criteria® are evidence-based guidelines for specific clinical conditions that are reviewed every two years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances where evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.517
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.002

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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueUltrasound QuarterlySame topicOvarian cancer diagnosis and treatmentFrench-language works237,207