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Record W4387530200 · doi:10.3390/diagnostics13203171

Approach to Imaging of Mediastinal Masses

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.952
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.111
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