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Record W2987951839 · doi:10.1016/s0167-8140(19)33381-x

9 Knowledge-Based Planning to Improve and Automate Patient-Specific Radiotherapy Quality Assurance Procedures in Clinical Trials - Secondary Analysis of CCTG HN6

2019· article· en· W2987951839 on OpenAlexaff
Srinivas Raman, Wendy R. Parulekar, Carrie-Lynne Swift, Robert Kosztyla, John Waldron, Jolie Ringash, Alysa Fairchild, Alexander Montenegro, Alan Nichol

Bibliographic record

VenueRadiotherapy and Oncology · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of AlbertaUniversity of TorontoQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsQuality assuranceMedical physicsQuality (philosophy)Radiation therapyRadiation treatment planningMedicineOncologyComputer scienceInternal medicinePathology

Abstract

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CARO-ASM 2019 radiotherapy (hypoRT) have shown promising outcomes.Large randomized trials comparing the modalities have yet to be completed.The objective of this study is to compare the provincial outcomes of patients treated with SBRT/hypoRT to those treated with surgery. Materials and Methods:We conducted a retrospective analysis of all provincial patients with Stage I NSCLC who underwent surgery or SBRT/hypoRT (biological equivalent dose of at least 84 Gy), from 2006 to 2016.Baseline patient, tumour, treatment and clinical outcome data were collected from the British Columbia Cancer Registry.Overall survival (OS) and cancer specific survival (CSS) were analyzed using Kaplan-Meier method.We further analyzed patients based on age, with 75 as cut off for older population.Cox regression analysis was performed to identify predictors of outcomes.Results: Among 1230 patients, 235 (19.1%) were treated with SBRT/hypoRT and 995 (80.9%) with surgery.158 (12.2%) patients received 48Gy in 4Fx, 42 (3.4%)60Gy in 8Fx, 35 (2.8%) 60Gy in 15Fx.783 (63.7%) patients underwent lobectomy, 28 (2.3%) pneumonectomy, 184 (15.0%) sub-lobar resection.The median age was 69, with 339 (27.6%) age >75.The median follow-up was 26.2 months for SBRT/hypoRT and 29.4 months for surgery.The two-and five-year OS were 73.8% and 27.7% for SBRT/hypoRT, and 78.4% and 36.1% for surgery, with no significant difference between the groups (p=0.182).Differences in OS were not significant among the radiotherapy dose fractionations (p=0.653) or the surgery types (p=0.193).Comparing SBRT/hypoRT to surgery, significant differences in OS were not detected for patients age <75 (p=0.299)nor >75 (p=0.224).The two-and five-year CSS were respectively 85.2% and 54.3% for SBRT/hypoRT, and 81.1% and 42.2% for surgery, with improved outcomes in the SBRT/hypoRT group (p=0.004).Patients age >75 had greater CSS with SBRT/ hypoRT (p<0.001), with no significant difference in patients age <75 (p=0.077).On multivariate analysis, there was no significant indicator of improved OS found for the SBRT/hypoRT group.For the surgery group, age <75 (p<0.001)predicted for better OS while ECOG (ref=0) 2 (p=0.041), 3 (p<0.001),4 (p<0.001)predicted for worse.Conclusions: At a registry level, SBRT/hypoRT achieves OS outcomes similar to surgery.Improved CSS is observed in the SBRT/ hypoRT cohort, notably for patients age >75.Further evaluation in large randomized studies is warranted.

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)0.038
metaresearch head score (Gemma)0.108
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: none
Teacher disagreement score0.041
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.108
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.002

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 head0.070
GPT teacher head0.391
Teacher spread0.321 · 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".

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Citations0
Published2019
Admission routes1
Has abstractno

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