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Record W2801261366 · doi:10.1016/j.ajoc.2018.04.019

Nodular fasciitis: A rapidly enlarging destructive periorbital mass in an infant

2018· article· en· W2801261366 on OpenAlexaff
R. Tom Liu, Erika Henkelman, Oana Popescu, Vivian T. Yin

Bibliographic record

VenueAmerican Journal of Ophthalmology Case Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsNodular fasciitisMedicineMalignancyFasciitisBiopsyBone erosionSoft tissueRadiologyPathology

Abstract

fetched live from OpenAlex

To review the clinical and histopathological features of nodular fasciitis, a rare benign periorbital tumor that mimics orbital malignancy, by presenting a case involving an infant with marked orbital wall erosion requiring repair. A 9-month-old boy developed a rapidly growing periorbital mass concerning for a soft tissue malignancy. Computerized tomography (CT) scans showed bony erosion of the lateral orbital wall. Incisional biopsy revealed nodular fasciitis. USP6 gene rearrangement was negative. The tumor was completely excised and the underlying orbital wall defect was repaired with polydioxanone (PDS) plate. Nodular fasciitis is a benign periorbital tumor that presents like malignancies and warrants prompt investigations, especially in children. Orbital wall erosion is rare and can be repaired to yield good functional and cosmetic outcome.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.322
Teacher spread0.306 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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