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Record W2274199756 · doi:10.1155/2016/4786498

Conjunctival Involvement of T-Cell Lymphoma in a Patient with Mycosis Fungoides

2016· article· en· W2274199756 on OpenAlexafffund
Sultan Aldrees, Pablo Zoroquiaín, Sarah Alghamdi, Patrick T Logan, Sonia Callejo, Miguel N. Burnier

Bibliographic record

VenueCase Reports in Ophthalmological Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicCutaneous lymphoproliferative disorders research
Canadian institutionsMcGill University
FundersMcGill University
KeywordsMycosis fungoidesMedicineConjunctivaDermatologyImmunophenotypingEyelidLymphomaPopulationPathologyBiopsySurgeryImmunologyAntigen

Abstract

fetched live from OpenAlex

Background. Ocular involvement in mycosis fungoides (MF) cases occurs in one-third of patients with the eyelid being the most frequent site affected; however, conjunctival involvement is rarely reported. Herein, we report a rare case of conjunctival involvement of MF. Case Presentation. A 66-year-old man who was previously diagnosed with MF in 2010 and was treated presented in 2014 complaining of foreign body sensation and redness in both eyes. Slit lamp examination of both eyes showed erythematous conjunctival growth that extended circumferentially. Physical examination revealed erythematous skin lesions on different body parts. Conjunctival biopsy was performed and revealed a dense, highly polymorphic lymphocytic population. The immunophenotype demonstrated a neoplastic T-cell origin consistent with MF. A diagnosis of conjunctival involvement by MF was made. The conjunctiva was treated with radiotherapy resulting in tumor regression. There were no recurrences at the 6-month follow-up. Conclusion. T-cell lymphoma should be considered in patients with a history of MF presenting with conjunctival and skin lesions.

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)0.002
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.309
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

Citations3
Published2016
Admission routes2
Has abstractyes

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