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Record W2327818228 · doi:10.1097/rlu.0b013e3181f9dec2

Intraocular Lymphoma and Central Nervous System Lymphoma Involving the Hypothalamus

2010· article· en· W2327818228 on OpenAlexaff
William Makis, Javier A. Novales-Diaz, Marc Hickeson

Bibliographic record

VenueClinical Nuclear Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineLymphomaBiopsyIntraocular lymphomaChemotherapyMethotrexateCentral nervous systemPathologyRadiation therapyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

A 77-year-old woman presented with sudden onset of loss of vision in the left eye and bilateral eye inflammation. Biopsy of the vitreous fluid of the right eye revealed large abnormal cells suggestive of lymphoma. A staging F-18 FDG PET/CT showed intense uptake in the hypothalamus, and mild focal uptake in the subcutaneous tissues of the right lower leg, which were biopsied to reveal diffuse large B cell lymphoma. The patient was treated with high dose chemotherapy (methotrexate), and a follow-up PET/CT showed complete resolution of the hypothalamic and right leg lesions. Intraocular-central nervous system lymphoma (also known as oculocerebral lymphoma) is rare, with only 150 cases reported in the literature. This case highlights the utility of PET/CT in the staging and evaluation of response to therapy for this rare lymphoma subtype. We recommend that patients with intraocular-central nervous system lymphoma, who have a staging PET/CT should have total body acquisitions that include the head and neck, and upper and lower extremities, as these may yield unexpected sites of disease, easily accessible for biopsy.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.296
Teacher spread0.272 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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