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Record W2281671786 · doi:10.1097/coc.0000000000000274

A Meta-Analysis of the Association Between Radiation Therapy and Survival for Surgically Resected Soft-Tissue Sarcoma

2016· review· en· W2281671786 on OpenAlexaff
Xuanlu Qu, Carrie C. Lubitz, Jennifer Rickard, Stephane G. Bergeron, Nabil Wasif

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2016
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsJewish General HospitalQueen's University
Fundersnot available
KeywordsMedicineOdds ratioHazard ratioConfidence intervalSoft tissue sarcomaRadiation therapyConfoundingMeta-analysisInternal medicinePopulationSarcomaSurgerySoft tissuePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: Radiotherapy for soft-tissue sarcoma (STS) has been shown to reduce local recurrence, but without clear improvement in survival. We conducted a meta-analysis to study the association between radiotherapy and survival in patients undergoing surgery for STS. METHODS: A systematic review was conducted from PubMed, EMBASE, Web of Science, and Cochrane databases. Our population of interest consisted of adults with primary extremity, chest wall, trunk, or back STS. Our metameters were either an odds or hazard ratio for mortality. A bias score was generated for each study based on margin status and grade. RESULTS: Of 1044 studies, 30 met inclusion criteria for final analysis. The pooled odds ratio in patients receiving radiation was 0.94 (95% confidence interval [CI], 0.78-1.14). The pooled estimate of the hazards ratio in patients receiving radiation was 0.87 (95% CI, 0.73-1.03) overall and 0.65 (95% CI, 0.52-0.82) for studies judged to be at low risk of bias. Significant publication bias was not seen. CONCLUSIONS: High-quality studies reporting adjusted hazard ratios are associated with improved survival in patients receiving radiotherapy for STS. Studies in which odds ratios are calculated from event data and those that do not report adjusted outcomes do not show the same association, likely due to confounding by indication.

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 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)0.006
metaresearch head score (Gemma)0.004
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 score0.736
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0120.007
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 head0.325
GPT teacher head0.521
Teacher spread0.197 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations12
Published2016
Admission routes1
Has abstractyes

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