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Discordance between central versus local response assessments in neuroendocrine tumor (NET) patients (pts) enrolled in A021202.

2021· article· en· W3123216051 on OpenAlexaff
Susan M. Geyer, Michelle R. Mahoney, Timothy R. Asmis, Nathan C. Hall, Sanja Karovic, Michael V. Knopp, Priya Kumthekar, Andrew B. Nixon, Eileen M. O’Reilly, Lawrence H. Schwartz, Jonathan Strosberg, Jeffrey A. Meyerhardt, Michael L. Maitland, Emily K. Bergsland

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsOttawa Hospital
FundersNational Institutes of Health
KeywordsMedicineConcordanceClinical endpointClinical trialInternal medicineProgressive diseasePazopanibRandomized controlled trialOncologyNuclear medicineDiseaseCancer

Abstract

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361 Background: Assessment of tumor response in extrapancreatic NETs with metastases can be very challenging. Previous studies suggest a high degree of discordance between local and central imaging reviews, which has implications for clinical practice and trial design. Methods: Serial images archived from a randomized phase II trial (A021202) of pazopanib vs placebo in progressive non-pancreatic NETs were evaluated by central review, with real-time review conducted at the time of locally interpreted progressive disease (PD). The primary endpoint of the trial was progression-free survival (PFS) by central review. Discordances between central (Alliance Imaging Core Laboratory) and local (investigator-reported) reviews were assessed. Scan-level and pt-level results across both treatment arms were evaluated. Kappa tests were used to test concordance based on source of review. Results: 151 pts had a total of 724 scans with response adjudication by both local and central RECIST review. Discordance was observed in both directions. Overall, 20% of scans (143/724) had discordant classifications. The most common discordances were: stable disease (SD) on local vs. PD on central review (82/143=57%), and PD on local vs. SD on central review (32/143=22%). On a pt level, 78 of 151 pts (52%) had discordant reviews; 8 had >1 type of discordance. Overall, 30% of pts (N=45) had a determination of PD on central review, but SD or better on local review, potentially resulting in excessive exposure to therapy. In contrast, 20% (N=30) were classified as PD on local read but SD or better on real-time central review (which did not necessarily translate into an abbreviated course of treatment). Cohen’s kappa statistics revealed only moderate concordance between local and central reviewers both at the scan (K=0.48, 95% CI: 0.42 – 0.55) and pt (K=0.41, 95% CI: 0.32 – 0.5) levels, with no significant influence by treatment arm, primary tumor site, tumor functionality, histology, differentiation or primary disease spread. Conclusions: Discordance was observed in both directions, where 30% of pts were potentially kept on study drug too long (based on central read), and 20% would have been taken off study treatment early for local PD were it not for real-time central review. Although this bidirectional discordance did not affect the overall findings of the PFS outcome between arms in the trial, these analyses highlight the high prevalence of discordance, the potential to negatively influence treatment duration in both directions, and the need for more straightforward methods of assessing treatment response in carcinoid. Support: U10CA180821, U10CA180882, U24CA196171; NETRF Investigator Award; https://acknowledgments.alliancefound.org Clinical trial information: NCT01841736.

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)0.011
metaresearch head score (Gemma)0.023
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.510
Teacher spread0.397 · 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
Published2021
Admission routes1
Has abstractyes

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