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Record W4377939150 · doi:10.1097/cco.0000000000000954

Next questions in the management of retroperitoneal sarcoma

2023· review· en· W4377939150 on OpenAlex
Ashley Drohan, Alessandro Gronchi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Opinion in Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSarcomaMEDLINEGeneral surgeryRadiologyPathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Retroperitoneal soft-tissue sarcomas (RPS) are a group of rare, histologically distinct tumours with variable recurrence patterns depending on histological type. This review will discuss the growing body of evidence supporting histology-specific, multidisciplinary management and highlight areas of future research for patients with RPS. RECENT FINDINGS: Histology-tailored surgery is the cornerstone of management in patients with localized RPS. Further efforts to develop resectability criteria and identify patients who will benefit from neoadjuvant treatment strategies will help standardize the treatment of patients with localized RPS. Surgery for local recurrence is well tolerated in selected patients and re-iterative surgery in liposarcoma (LPS) may be beneficial at the time of local recurrence. The management of advanced RPS holds promise with several trials currently investigating systemic treatment beyond conventional chemotherapy. SUMMARY: The management of RPS has made significant progress over the past decade owing to international collaboration. Ongoing efforts to identify patients who will derive the most benefit from all treatment strategies will continue to advance the field of RPS.

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.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.286
GPT teacher head0.500
Teacher spread0.214 · 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