Smaller hippocampal subfield volumes predict verbal associative memory in pediatric brain tumor survivors
Bibliographic record
Abstract
The developing hippocampus is highly sensitive to chemotherapy and cranial radiation treatments for pediatric cancers, yet little is known about the effects that cancer treatents have on specific hippocampal subfields. Here, we examined hippocampal subfield volumes in 29 pediatric brain tumor survivors treated with cranial radiation and chemotherapy, and 30 healthy developing children and adolescents. We also examined associations between hippocampal subfield volumes and short-term verbal memory. Hippocampal subfields (Cornus Ammonis (CA) 1, CA2-3, dentate gyrus (DG)-CA4, stratum radiatum-lacunosum-moleculare, and subiculum) were segmented using the Multiple Automatically Generated Templates for Different Brains automated segmentation algorithm. Neuropsychological assessment of short-term verbal associative memory was performed in a subset of brain tumor survivors (N = 11) and typically developing children (N = 16), using the Children's Memory Scale or Wechsler's Memory Scale-third edition. Repeated measures analysis of variance showed that pediatric brain tumor survivors had significantly smaller DG-CA4, CA1, CA2-3, and stratum radiatum-lacunosum-moleculare volumes compared with typically developing children. Verbal memory performance was positively related to DG-CA4, CA1, and stratum radiatum-lacunosum-moleculare volumes in pediatric brain tumor survivors. Unlike the brain tumor survivors, there were no associations between subfield volumes and memory in typically developing children and adolescents. These data suggest that specific subfields of the hippocampus may be vulnerable to brain cancer treatments, and may contribute to impaired episodic memory following brain cancer treatment in childhood.
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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".