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Retroperitoneal sarcomas: combined modality treatment approaches

2002· review· en· W2315212411 on OpenAlexaff
Peter W. T. Pisters, Brian O’Sullivan

Bibliographic record

VenueCurrent Opinion in Oncology · 2002
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineSarcomaModality (human–computer interaction)RadiologySoft tissue sarcomaSoft tissueTreatment modalityMetastasisDistant metastasisSurgeryCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

Retroperitoneal soft tissue sarcomas are rare tumors estimated to account for 15%of all patients with soft tissue sarcoma seen in referral populations. The standard of care for patients with localized, resectable retroperitoneal sarcomas is surgical resection with gross and microscopically negative margins. However, owing to the large size and locally advanced nature of these tumors, this goal is difficult to achieve in most patients. As a result, the disease is characterized by a high propensity for local recurrence and a grade-specific risk for distant metastasis. Over the past decade, there has been considerable research into combined modality treatment of these tumors. The present report outlines current concepts relating to the diagnosis, staging, and management of retroperitoneal sarcomas. Emphasis is placed on evolving combined modality treatment approaches and current investigational strategies.

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)0.000
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.444
GPT teacher head0.466
Teacher spread0.021 · 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

Citations88
Published2002
Admission routes1
Has abstractyes

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