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Record W2967200533 · doi:10.15353/cjo.v81i2.436

A Clinical Masquerader: Squamous Cell Carcinoma of the Eyelid Previously Diagnosed as an Eye Bump

2019· article· en· W2967200533 on OpenAlexvenueno aff
Sanjeet Kaur Virk, A.J. Fisher, Brian D. Fisher, Alexis Rodriguez

Bibliographic record

VenueCanadian Journal of Optometry · 2019
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEyelidMalignancyLesionBiopsyRadiologyDermatologyPathology

Abstract

fetched live from OpenAlex

Malignant eyelid tumors are often difficult to diagnose at early stage growth, and can be clinically challenging. Due to the high prevalence of periocular skin cancers, clinicians must be very attentive in their assessment of skin lesions amongst their patients. This case report highlights an early non-healing eyelid lesion transforming into squamous cell carcinoma. An 83-year-old male with no history of malignancy presented with a non-healing and rapidly growing lesion of the left lower eyelid. He first noticed this lesion one-month prior and was treated with oral antibiotics without improvement by his primary care provider. Our slit lamp examination of the left eyelid revealed a large ulcerated mass with white mucoid discharge draining from the center of the lesion. After an oculoplastics referral, the patient was diagnosed with squamous cell carcinoma confirmed by biopsy. Computed tomography(CT) showed no metastasis or invasion to deep layer tissue. The management decision in this case required exenteration of the left eye socket followed by radiation therapy. This case illustrates the clinical course and invasive nature of periocular squamous cell carcinoma. It can present in a variety of different appearances, but are mostly painless, hyperkeratotic lesions that progressively change and ulcerate. An extensive history and careful clinical examination are vital in order to detect malignancy in a timely manner.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.333
Teacher spread0.318 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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