CMS-03RISK FACTORS FOR LONG TERM SPEECH DEFICITS IN CHILDREN WITH CEREBELLAR MUTISM SYNDROME
Bibliographic record
Abstract
Cerebellar mutism syndrome (CMS) occurs in 25% of children who undergo posterior fossa tumor (PFT) resection. Although long-term speech effects have been observed and children without CMS can exhibit speech difficulties, little has been considered regarding the impact of duration and severity of acute CMS symptoms and later speech/language difficulties between those with and without CMS. We examined children with post-surgical changes in communication with and without CMS to investigate subsequent motor speech difficulties. We also considered the impact of CMS onset, duration, and diagnosis age on speech outcome with the goal of predicting prolonged speech difficulties based on communication change. Data were collected retrospectively from 36 children who had speech/language changes following resection of a PFT, comprising two groups – those with CMS (n = 23) and those without CMS (n = 13). Motor speech and communication were assessed at two points postoperatively. Speech difficulties amongst the two groups were similar. Significant differences were evident in the acute period for respiration (p = .036), and sequential motion rates (SMR) (p = .003), and at 1-year postoperatively for SMR(p = .009). Longer duration of CMS was evident for patients with severe communication change (p = .019), relative to those with moderate change. Pooling the groups, older age at diagnosis and duration of mutism were significantly correlated with number of speech difficulties(r = .717,p < .001 and r = .506,p = .01, respectively). Results indicate that SMR as a gauge of apraxia may play a critical role in determining long-term speech difficulties in children with CMS. Age, severity and duration of mutism may also be crucial in predicting later speech difficulties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".