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Record W4309016097 · doi:10.1093/neuonc/noac209.756

NCOG-03. IMPACT OF THE RATE OF RADIOGRAPHIC RESPONSE (RR) OF BRAIN METASTASES (BM) TO WHOLE BRAIN RADIATION THERAPY (WBRT) ON NEUROCOGNITIVE FUNCTION (NCF) ON NRG-CC001

2022· article· en· W4309016097 on OpenAlexaff
Kiran Devisetty, Stephanie Pugh, Paul D. Brown, Vinai Gondi, Jeffrey S. Wefel, Abhishek A. Solanki, Tomy Kalapparambath, Grant Harmon, Anjali L. Saripalli, Brian Chou, Bhanu Prasad Venkatesulu, Thomas Boike, Vijayananda Kundapur, David Roberge, Joseph Bovi, Mackenzie McGee, Tim J. Kruser, Kenneth Y. Usuki, Minesh P. Mehta, Lisa A. Kachnic

Bibliographic record

VenueNeuro-Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsCentre Hospitalier de l’Université de MontréalSaskatchewan Cancer Agency
Fundersnot available
KeywordsMedicineInternal medicineNeurocognitiveRandomized controlled trialLogistic regressionWhole brain radiotherapyClinical trialOncologyCancerCognitionBrain metastasis

Abstract

fetched live from OpenAlex

Abstract BACKGROUND The assessment of BM response to WBRT and its impact on NCF in clinical trials has been limited by lack of standardized imaging protocols. NRG-CC001 is a randomized clinical trial requiring pre-specified MRI protocols at baseline and 6-months, providing a uniform dataset to investigate if RR correlates with NCF changes. METHODS NRG-CC001 randomized patients with BM to hippocampal avoidance WBRT (HA-WBRT) or WBRT. NCF was analyzed using 6-month standardized change scores and deterioration, defined using the reliable change index. Chi-square and t-tests were used for pretreatment characteristic comparisons. Inter-rater reliability between central and institutional assessment of RR was assessed with weighted kappa, κ. Linear regression was used to test trends in NCF change scores across types of response and multivariable logistic regression was used to test the association of RR to NCF deterioration. RESULTS 149 and 135 patients were evaluable for RR and NCF assessment, respectively. Pretreatment characteristics were well-balanced, except for post-high school education (70.6% HA-WBRT vs. 52.5% WBRT, p=0.023). Inter-rater reliability between central and institutional assessment of RR was fair (κ=0.36). There was no difference between arms in RR (p=0.41) with overall rates of 14.1% CR, 42.2% PR, 17% SD, and 26.7% PD. Patients with CR had improved 6-month NCF change as measured by HVLT-R Total Recall (p=0.0005), HVLT-R Delayed Recall (p=0.0003), HVLT-R Delayed Recognition (p=0.011), TMT-B (p=0.033), COWA (p=0.016), and Clinical Trial Battery Composite score (p=0.0011). Multivariable analysis demonstrated less deterioration in HVLT-R Delayed Recall for CR (p=0.019) and PR (p=0.0086) vs. SD/PD and HVLT-R Recognition for PR (p=0.031) vs. SD/PD. CONCLUSIONS HA-WBRT and WBRT result in similar RR at 6-months. CR or PR is associated with better NCF preservation. This suggests investigation into treatment escalation for patients with SD/PD may provide further NCF benefit along with HA-WBRT and memantine.Grant support from NCI-UG1CA189867 and U24CA180803.

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)0.006
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.357
Teacher spread0.325 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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