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Record W4387530200 · doi:10.3390/diagnostics13203171

Approach to Imaging of Mediastinal Masses

2023· review· en· W4387530200 on OpenAlexaff
Jitesh Ahuja, Chad D. Strange, Rishi Agrawal, Lauren T. Erasmus, Mylene T. Truong

Bibliographic record

VenueDiagnostics · 2023
Typereview
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsMcGill University
Fundersnot available
KeywordsMediastinal massMagnetic resonance imagingMalignancyRadiologyComputed tomographyMedicineDifferential diagnosisMedical imagingPathology

Abstract

fetched live from OpenAlex

Mediastinal masses present a diagnostic challenge due to their diverse etiologies. Accurate localization and internal characteristics of the mass are the two most important factors to narrow the differential diagnosis or provide a specific diagnosis. The International Thymic Malignancy Interest Group (ITMIG) classification is the standard classification system used to localize mediastinal masses. Computed tomography (CT) and magnetic resonance imaging (MRI) are the two most commonly used imaging modalities for characterization of the mediastinal masses.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.111
GPT teacher head0.387
Teacher spread0.276 · 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
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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