Reliable change in pediatric brain tumor: A preliminary investigation
Bibliographic record
Abstract
Children treated for brain tumor show evidence of declines in general intellectual abilities (i.e., IQ). Group-level data indicate subtle declines over time on average, but no study has utilized a clinical criterion to identify and describe a reliable change in survivors of pediatric brain tumor (PBT). In this study, we discuss the utility of reliable change index (RCI) methodology to supplement group-level analysis (e.g., repeated measures ANOVA). This pilot sample consisted of 22 children (M age = 10.47 years) treated for PBT who completed initial and follow-up assessments (M interval = 23.58 months). Cognitive data included composite scores from the WISC-IV. An RCI z-score was calculated for each participant on each composite score based on two different test–retest reliability coefficients. As a group, survivors of PBT did not demonstrate a statistically significant change from initial to follow-up on any WISC-IV composite score. When RCI was calculated based on reliability coefficients with shorter test–retest intervals provided by the test publisher, 77% of survivors demonstrated a reliable change in performance on at least one measure. The frequency of RCI decreases in working memory was significantly higher than expected. In contrast, only 32% of survivors showed reliable changes on at least one measure when RCI was based on a reliability coefficient derived from a clinical sample with a longer retest interval. This study demonstrates that highly divergent results may be obtained with RCI and the importance of the source of reliability estimates.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".