Recovery of intrinsic cognitive weakness in successive processing after bypass surgery for pediatric moyamoya disease
Bibliographic record
Abstract
Abstract Background Successive processing, a form of working memory function detected with the Das Naglieri Cognitive Assessment System (CAS), is selectively impaired in pediatric moyamoya disease (MMD). We aimed to test whether successive processing in children with MMD was improved after bypass surgery under the control of confounding. Methods The present retrospective cohort study included children with MMD who underwent direct or combined bypass surgery. Neuropsychological tests including the CAS were administered at two timepoints, before and after surgery, approximately one year apart. The least squares (LS) mean standard score and LS mean difference between timepoints were calculated using a mixed model for repeated measures, which included five clinical factors along with the timepoint. Models including an interaction term were also generated to assess the effect of each clinical factor. Cognitive intra-individual variability across four domains of the CAS was assessed with an analysis of variance at each timepoint. Results Of 60 patients who underwent surgery, 42 fulfilled the inclusion criteria. The median duration between assessments was 15 months. The LS mean standard scores of successive processing increased after surgery (LS mean, 95.8 versus 100.2; LS mean difference, 4.4 [95% CI, 1.5–7.3]; P = 0.004). The increase was more pronounced in those with younger age at onset, shorter delay before surgery, pre-existing infarct, posterior cerebral artery involvement, and severer ischemic stage before surgery. Intra-individual variability, shown as the lowest score of successive processing at baseline, resolved after surgery ( F = 3.56, P = 0.016 versus F = 1.21, P = 0.31). Successive processing was the domain most likely to be improved after surgery. Conclusions The present results suggest that successive processing is improved after bypass surgery. Larger and longer follow-up studies are required to confirm the influencing factors and long-term effect.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".