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Record W2030360007 · doi:10.1097/rct.0b013e3181e39755

Angiomatoid Fibrous Histiocytoma

2010· article· en· W2030360007 on OpenAlexaff
Amr M. Ajlan, Karl Sayegh, Tom Powell, Haegert David, Jackie Khan, Ayoub Nahal

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2010
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineHemosiderinMagnetic resonance imagingPresentation (obstetrics)Soft tissuePathologyRadiologyAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: Angiomatoid fibrous histiocytoma (AFH) is a rare soft tissue tumor usually affecting the extremities of children and young adults and has a favorable prognosis. The purpose of this study was to report the magnetic resonance imaging appearance in 2 cases of AFH. CONCLUSIONS: Although the overall features of AFH can be confused with other subtypes of malignant fibrous histiocytoma, the young age at presentation, the location of the mass, the presence of intralesional blood-filled cystic spaces with fluid-fluid levels, associated features of hemosiderin deposition, and an enhancing fibrous pseudocapsule are all potential hints favoring this entity.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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 designCase report
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

Citations20
Published2010
Admission routes1
Has abstractyes

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