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Record W2141205256 · doi:10.1586/14737140.2015.1096203

Optimal therapy for desmoid tumors: current options and challenges for the future

2015· review· en· W2141205256 on OpenAlexaff
Mushriq Al‐Jazrawe, Magdalene Au, Benjamin A. Alman

Bibliographic record

VenueExpert Review of Anticancer Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsMedicineWatchful waitingRadiation therapyFibromatosisCryoablationChemotherapyOncologyInternal medicinePathologyCancer

Abstract

fetched live from OpenAlex

Desmoid tumors, or aggressive fibromatosis, are rare, locally infiltrative neoplasms caused by mutations that activate β-catenin. Although these tumors do not metastasize, they are difficult to manage due to variability in tumor presentation and behavior. A variety of treatment options exist, including surgery, radiotherapy, chemotherapy, hormone therapy, isolated limb perfusion, cryoablation and tyrosine kinase inhibitors. Treatment-induced morbidity and poor local control rates, combined with spontaneous stabilization of some desmoid tumors, have allowed watchful waiting to recently emerge as a front-line management option. This has emphasized the need to better understand tumor behavior in order to differentiate between tumors that may stabilize and those that may progress. Here, we review the most recent findings in desmoid tumor biology and treatment options for this enigmatic disease.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.180
GPT teacher head0.464
Teacher spread0.284 · 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 designNot applicable
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

Citations41
Published2015
Admission routes1
Has abstractyes

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