Bibliographic record
Abstract
Importance of Formal Neurocognitive Testing 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). Impaired neurocognitive functioning occurs in the majority of patients with central nervous system (CNS) tumors and has been shown to be impaired by cancer therapies for tumors arising outside the brain (Meyers et al ., 1995; Wefel et al ., 2004b).
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".