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Record W1527945681 · doi:10.1017/cbo9780511545900.024

Neurocognitive testing in clinical trials

2008· book-chapter· en· W1527945681 on OpenAlexaff
Jennifer A. Smith, Jeffrey S. Wefel

Bibliographic record

VenueCambridge University Press eBooks · 2008
Typebook-chapter
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeurocognitiveMedicineClinical endpointClinical trialQuality of life (healthcare)OncologyCancerFood and drug administrationClinical researchSurrogate endpointInternal medicineIntensive care medicinePsychiatryPharmacologyCognition

Abstract

fetched live from OpenAlex

Since the early 1990s it has been recognized that the “net clinical benefit” of a therapy includes not only traditional survival endpoints but also benefits in terms of symptoms and quality-of-life endpoints (O'Shaughnessy et al., 1991). With increasing awareness that it is often inadequate to measure survival without consideration of the “quality” of that survival, there has been a call to develop and include neurocognitive and patient-reported outcome (PRO) measures into modern trial design. Members of the Food and Drug Administration (FDA), National Cancer Institute (NCI), American Association for Cancer Research (AACR), and American Society of Clinical Oncology (ASCO) met in 2006 to discuss endpoints for drug registration trials in primary brain cancer. The recommendations generated from this meeting were provided for the Oncology Drug Advisory Committee's (ODAC) consideration and included a composite progression endpoint in which radiographical, neurocognitive, neurological, and PRO are jointly considered (http://www.fda.gov/cder/drug/cancer_endpoints/brain_summary.pdf; accessed 10 April, 2008). The FDA has recently opined that a therapeutic agent may be approvable if preservation of neurocognitive function can be demonstrated even if survival endpoints are equivalent (minutes of an end-of-phase-II meeting regarding a novel radiation sensitizing agent, October 21, 1998).

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.196
GPT teacher head0.342
Teacher spread0.146 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2008
Admission routes1
Has abstractyes

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