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Record W4415992248 · doi:10.5603/rpor.107741

Assessment of toxicity in patients with ultra-central thoracic tumours treated with stereotactic body radiotherapy with a dose of 50 Gy in 5 fractions

2025· article· en· W4415992248 on OpenAlexaff
Rie Nadia Asso, Neil Kopek, Marie Duclos, Bassam Abdulkarim, Tanner Connell, M. Perna, Sergio Faria

Bibliographic record

VenueReports of Practical Oncology & Radiotherapy · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsToxicityAcute toxicityCohortRadiation therapyRadiation doseDose fractionation

Abstract

fetched live from OpenAlex

Background: Stereotactic body radiotherapy (SBRT) is a well-accepted treatment for metastatic and primary lung cancer; however, an optimal regimen is still unclear for ultra central thoracic lesions. The objective of this manuscript is to report the toxicity of SBRT in patients with ultra-central tumors treated with 50 Gy in 5 fractions. Materials and methods: We performed a retrospective review of patients with ultra-central lung lesions treated with SBRT in our institution at the dose of 50 Gy in 5 fractions, delivered every other day. Lesions were defined as ultra-central when the planning target volume (PTV) overlapped the trachea, proximal bronchial tree, great vessels, heart and esophagus. Constraints for organ at risk (OAR) were the ones used in the RTOG-0813 trial. Results: 86 patients were included in this review. The median age was 74 years. The overlapping OAR were: the great vessels in 46 patients (53.4%), heart in 20 (23.2%), tracheobronchial tree in 18 (20.9%) and esophagus in 2 (2.3%). Median follow up was 17 months. The median overall survival was 39 months. There was no SBRT related grade 3 or greater acute or late toxicity. Conclusion: In this cohort of patients with ultra-central thoracic lesions treated with 50 Gy in 5 fraction SBRT, no grade 3-5 acute or late toxicity was observed.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.800

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 integrity0.0000.000
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 head0.011
GPT teacher head0.374
Teacher spread0.362 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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