The Working Memory Network and Its Association with Working Memory Performance in Survivors of non-CNS Childhood Cancer
Bibliographic record
Abstract
Childhood cancer and its treatment puts survivors at risk of low working memory capacity. Working memory represents a core cognitive function, which is crucial in daily life and academic tasks. The aim of this functional MRI (fMRI) study was to examine the working memory network of survivors of childhood cancer without central nervous system (CNS) involvement and its relation to cognitive performance. Thirty survivors (aged 7–16 years, ≥ 1 year after cancer treatment) and 30 healthy controls performed a visuospatial working memory task during MRI, including a low- and a high-demand condition. Working memory performance was assessed using standardized tests outside the scanner. When cognitive demands increased, survivors performed worse than controls and showed evidence for slightly atypical working memory-related activation. The survivor group exhibited hyperactivation in the right-hemispheric superior parietal lobe (SPL) in the high- compared to the low-demand working memory condition, while maintaining their performance levels. Hyperactivation in the right SPL coincided with poorer working memory performance outside the scanner in survivors. Even in survivors of childhood cancer without CNS involvement, we find neural markers pointing toward late effects in the cerebral working memory network.AbbreviationsfMRI: Functional magnetic resonance imaging; CNS: Central nervous system; MNI: Montreal Neurological Institute; SES: Socioeconomic status; SPL: Superior parietal lobe
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".