MétaCan
Menu
Back to cohort
Record W2148298317 · doi:10.2310/7750.2014.13192

A Clinicopathologic Review of a Case Series of Dermatofibrosarcoma Protuberans with Fibrosarcomatous Differentiation

2015· review· en· W2148298317 on OpenAlexaffabout
Paul Kuzel, Muhammad N. Mahmood, Andrei I. Metelitsa, Thomas G. Salopek

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2015
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsSKiN HealthUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsDermatofibrosarcoma protuberansMedicineLymphovascular invasionMetastasisPathologyDermatofibrosarcomaPerineural invasionDermatologyCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Dermatofibrosarcoma protuberans with fibrosarcomatous differentiation (DFSP-FS) is a rare variant of DFSP with a more aggressive clinical course, characterized by higher rates of local recurrence, metastasis, and death. METHODS: We conducted a clinicopathologic review of all DFSP-FS cases that occurred in Alberta, Canada, from 1997 to 2007. RESULTS: Of the 75 DFSP cases reviewed, 4 demonstrated fibrosarcomatous differentiation. Three patients were female and one was male, and the age range was 25 to 76 years. Three tumors invaded to skeletal muscle, whereas one invaded to subcutaneous tissue only. Although perineural invasion was noted in all four cases, none exhibited lymphovascular space invasion. One local recurrence developed, and two of four tumors metastasized. Metastasis was associated with tumor size, tumor necrosis, grenz zone involvement, ulceration, thickness, and tumor grade. One patient died within 5 years of diagnosis. CONCLUSION: DFSP-FS represents a more aggressive subtype of DFSP. Several features of DFSP-FS may impart a higher risk of metastasis.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.084
GPT teacher head0.353
Teacher spread0.269 · 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
GenreReview

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

Citations18
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cutaneous Medicine and SurgerySame topicSarcoma Diagnosis and TreatmentFrench-language works237,207