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Record W1854608156 · doi:10.1002/jso.22128

Atypical fibroxanthoma: Clinicopathologic determinants for recurrence and implications for surgical management

2011· article· en· W1854608156 on OpenAlexaffabout
John S. Davidson, Daniel Demsey

Bibliographic record

VenueJournal of Surgical Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineAtypical fibroxanthomaDermisDistant metastasisPresentation (obstetrics)MetastasisHead and neckDermatologySurgeryPathologyImmunohistochemistryCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Atypical fibroxanthoma (AFX) is an uncommon skin tumor occurring primarily in the head and neck region of elderly Caucasian males. Treated with local excision, the disease is thought to be fairly benign but can occasionally demonstrate aggressive local recurrence as well as distant metastasis. METHODS: Seventy-one cases of AFX were reviewed, representing all patients presenting to the Health Sciences Centre of South Eastern Ontario with the diagnosis of AFX in the period 1989-2008. Demographic and pathologic data were obtained from patient charts for analysis. RESULTS: Mean age at presentation was 76, with a male:female ratio of 4:1. Recurrence occurred in 10 patients after an average period of 7.3 months. Three recurrent lesions went on to distant metastasis, on average 14.3 months after initial presentation. The remaining 60 tumors did not recur. Histologically, tumor extending beyond the dermis into the underlying adipose and muscular tissue had a 29.4% chance of local recurrence and an 11.8% chance of metastasis compared to lesions confined to the dermis only (9.3% and 1.8%). CONCLUSIONS: While the majority of AFX presentations are benign, there is a real possibility of metastatic spread. Invasion beyond the dermis and a rapid rate of recurrence are suggestive of a more aggressive clinical course.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.127
GPT teacher head0.419
Teacher spread0.292 · 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 designOther design
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

Citations38
Published2011
Admission routes2
Has abstractyes

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