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Record W4410206517 · doi:10.1093/bjro/tzaf009

A diagnostic approach to mediastinal masses in clinical practice

2024· review· en· W4410206517 on OpenAlexaff
Rebecca Mura, Svitlana Pochepnia, Daria Kifjak, Natallia Khenkina, Helmut Prosch

Bibliographic record

VenueBJR|Open · 2024
Typereview
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsMediastinal massClinical PracticeMedicineRadiologyMedical physicsFamily medicine

Abstract

fetched live from OpenAlex

Mediastinal masses represent a heterogeneous group of entities characterized by a variety of histopathological and radiological features. Imaging plays a pivotal role in the detection and interpretation of mediastinal abnormalities. CT remains the modality of choice due to its high spatial and temporal resolution and its ability to assess tissue composition, including the detection of fluid, fat, and calcifications. MRI represents a complementary tool in specific scenarios, such as differentiating complicated cysts from solid lesions or identifying intracellular fat content, as seen in thymic hyperplasia. The differential diagnosis of mediastinal masses relies primarily on the location of the mass and tissue composition, integrated with clinical characteristics of the patient. This review discusses the most common mediastinal masses in adults, providing a practical approach to their differentiation mainly based on the predominant density pattern and location.

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)0.003
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.298
GPT teacher head0.555
Teacher spread0.257 · 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 teacher head, not a consensus.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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