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Record W4288045947 · doi:10.1016/j.adro.2022.101037

3’-Deoxy-3’-(18F) Fluorothymidine Positron Emission Tomography/Computed Tomography in Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy: A Pilot Study

2022· article· en· W4288045947 on OpenAlexafffund
Shirley Lewis, Matthew Chan, Jessica Weiss, Hamid Raziee, Brandon Driscoll, Andrea Bezjak, A. Sun, Benjamin Lok, Douglass Vines, John Cho, Jean‐Pierre Bissonnette, Srinivas Raman, Andrew Hope, Meredith Giuliani

Bibliographic record

VenueAdvances in Radiation Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity Health NetworkUniversity of British ColumbiaPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of Toronto
FundersAstraZeneca CanadaAstraZenecaBristol-Myers Squibb
KeywordsMedicineNuclear medicineLung cancerPositron emission tomographyCohortStandardized uptake valueRadiation therapyRadiologyLungInternal medicine

Abstract

fetched live from OpenAlex

Purpose: The primary objective was to compare 3'-deoxy-3'-( 18 F) fluorothymidine (FLT) positron emission tomography (PET)/ computed tomography (CT) uptake in 3 cohorts of stereotactic body radiation therapy (SBRT) patients: (1) pre-SBRT, (2) stable post-SBRT lung fibrosis, and (3) suspicious or proven local recurrence post-SBRT. The secondary objectives were to optimize FLT-PET imaging by comparing FLT uptake in respiratory-gated (4-dimensional) versus nongated (3-dimensional) FLT-PET scans. Methods: Patients with early-stage non-small cell lung cancer planned or treated with SBRT at the institution with radiographic findings of fibrosis or recurrence were eligible for the study. All patients underwent imaging with FLT-PET/CT before SBRT in cohort 1 and at fibrosis or recurrence in cohort 2 and 3, respectively. The planned sample size was 20 patients in each cohort, with 60 patients total. FLT-PET standardized uptake value (SUV) variables including SUV max , SUV mean , SUV peak , SUV 50 , and SUV 95 were compared

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 score0.059
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.012
GPT teacher head0.329
Teacher spread0.317 · 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
Published2022
Admission routes2
Has abstractyes

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