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Record W4388588806 · doi:10.1093/neuonc/noad179.0014

BIOM-03. LACK OF CORRELATION OF MR-DETERMINED WHITE MATTER INJURY(WMI)WITH NEUROCOGNITIVE FUNCTION (NCF)6 MONTHS FOLLOWING WBRT+/-HIPPOCAMPAL AVOIDANCE(HA)+MEMANTINE:SECONDARY ANALYSIS OF NRG ONCOLOGY NRGCC001

2023· article· en· W4388588806 on OpenAlexaff
Joseph Bovi, Stephanie L. Pugh, Paul D. Brown, Vinai Gondi, Clifford G. Robinson, Jeffrey S. Wefel, David S. Sabsevitz, Markus Sprenger, Vijayananda Kundapur, David Roberge, Kiran Devisetty, Sunjay Shah, Kenneth Y. Usuki, Baldassarre Stea, Harold Yoon, Eric D. Donnelly, Nadia N. Laack, Rebecca Paulus, Lisa A. Kachnic

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMemantineMedicineNeurocognitivePost-hoc analysisOncologyInternal medicineTrail Making TestFluid-attenuated inversion recoveryNuclear medicineMagnetic resonance imagingRadiologyNMDA receptorCognitionCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

Abstract PURPOSE Previous analysis of NRG-CC001 suggested pre-treatment WMI volume was a significant imaging-biomarker predictor of post-treatment neurocognitive decline at 4-and 12-months following HA-WBRT+Memantine. This suggested patients with greater pre-treatment WMI were more susceptible to neurocognitive decline, specifically when undergoing HA-WBRT, but not standard-WBRT. The current analysis examined the relationship between changes in MR-determined WMI and NCF 6-months following WBRT+memantine+/-HA. METHODS NCF-testing was performed at baseline, 2, 4, 6, and 12 months post-WBRT, and included Hopkins Verbal Learning Test–Revised (HVLT-R), Trail Making Test (TMT) Parts A and B, and Controlled Oral Word Association (COWA). Pre-treatment WMI was measured by FLAIR-volume corrected for whole brain volume and corrected for the FLAIR volume associated with metastases (met vol) (FLAIR-volume/(whole brain volume–met-vol). Pearson correlation coefficients were used to assess the correlation between pre-treatment WMI and change from baseline to 6-months for each standardized NCF score. RESULTS Of the 518 randomized patients, 443 (217 patients WBRT+Memantine and 226 HA-WBRT+Memantine) had WMI data available at baseline, and of those, 162 patients (89 patients WBRT+Memantine and 73 HA-WBRT+Memantine) had both baseline and 6-month imaging available. For these patients, there was only a trend toward significance for change in corrected FLAIR-volume from baseline to 6-months (p = 0.174) but no significant change in met-vol (p = 0.438) or whole brain volume (p = 0.977). The change in standardized TMT Part A and B and the change in composite 6-month change scores were moderately correlated with change in corrected FLAIR volume adjusted for met-vol but only in the WBRT+Memantine arm (rho=-0.35, -0.34, -0.39 and p-value = 0.003, 0.006, 0.001). CONCLUSIONS Despite an increase in post-WBRT MR-WMI at 6-months, analysis did not show strong correlation between NCF and MR-WMI as determined by FLAIR-volume change between baseline and 6-months. Similar analyses are underway for the NRG-CC003 imaging data.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.313
Teacher spread0.289 · 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
Published2023
Admission routes1
Has abstractyes

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