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Record W4318755798 · doi:10.1002/uog.26129

<scp>ISUOG</scp> Practice Guidelines (updated): performance of fetal magnetic resonance imaging

2023· article· en· W4318755798 on OpenAlexaff
Daniela Prayer, G. Malinger, Luc De Catte, B. De Keersmaecker, Luís F. Gonçalves, Gregor Kasprian, Sherelle Laifer‐Narin, Wesley Lee, A. Millischer, Lawrence D. Platt, Florian Prayer, D. Pugash, Laurent Salomon, Magdalena Sanz Cortés, F. Stuhr, Ilan E. Timor‐Tritsch, B. Tutschek, Diane M. Twickler, Nick Raine‐Fenning

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2023
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsB.C. Women's Hospital & Health CentreUniversity of British Columbia
Fundersnot available
KeywordsMedicineMagnetic resonance imagingObstetricsRadiology

Abstract

fetched live from OpenAlex

The International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) is a scientific organization that encourages sound clinical practice and high-quality teaching and research related to diagnostic imaging in women's healthcare.The ISUOG Clinical Standards Committee (CSC) has a remit to develop Practice Guidelines and Consensus Statements as educational recommendations that provide healthcare practitioners with a consensus-based approach, from experts, for diagnostic imaging.They are intended to reflect what is considered by ISUOG to be the best practice at the time at which they are issued.Although ISUOG has made every effort to ensure that Guidelines are accurate when issued, neither the Society nor any of its employees or members accepts liability for the consequences of any inaccurate or misleading data, opinions or statements issued by the CSC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.142
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.142
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.009
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0050.004
Research integrity0.0110.008
Insufficient payload (model declined to judge)0.0620.048

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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations125
Published2023
Admission routes1
Has abstractyes

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