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Record W2156732883 · doi:10.2310/7750.2008.07076

Results of Radiation Therapy for Treatment of Classic Kaposi Sarcoma

2009· article· en· W2156732883 on OpenAlexaff
David Hauerstock, William Gerstein, T. Vuong

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2009
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineRadiation therapySarcomaSurgeryLesionChemotherapyRadiologyDermatologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Classic Kaposi sarcoma (CKS) is a vascular neoplasm that primarily affects men of Mediterranean and Ashkenazi Jewish descent. A variety of therapeutic options exist, and choice of treatment depends on clinical form and stage, as well as lesion location and size; options include surgical excision, intralesional interferon alpha-2b, local or extended field radiotherapy, and chemotherapy. OBJECTIVE: The aim of this study was to review the outcome of radiation therapy in the treatment of CKS at a single institution. METHODS: This retrospective study reviewed patients who receive radiation therapy for histologically confirmed CKS between 1994 and 2006. RESULTS: Sixteen patients were reviewed; the mean age at diagnosis was 74 years, and 13 patients were male. Fifteen patients (94%) presented with leg lesions, and two patients (12.5%) presented with arm lesions. The most commonly prescribed radiation dose was 30 Gy in 15 daily fractions of 2 Gy. All lesions responded to treatment, with a complete response rate of 88% and a partial response rate of 12%. Toxicity was limited to grade I dermatitis (four patients) and grade II dermatitis (two patients). CONCLUSION: Radiation therapy is an effective treatment modality for CKS and is associated with minimal toxicity.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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 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

Citations34
Published2009
Admission routes1
Has abstractyes

Explore more

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