Computerized Symbol Digit Modalities Test in a Swiss Pediatric Cohort – Part 2: Clinical Implementation
Bibliographic record
Abstract
Background Information processing speed (IPS) is a marker for cognitive function. It is associated with neural maturation and increases during development. Traditionally, IPS is measured using paper and pencil tasks requiring fine motor skills. Such skills are often impaired in patients with neurological conditions. Therefore, an alternative that does not need motor dexterity is desirable. One option is the computerized symbol digit modalities test (c-SDMT), which requires the patient to verbally associate numbers with symbols. Methods Eighty-six participants (8–16 years old; 45 male; 48 inpatients) were examined, 38 healthy and 48 hospitalized for a non-neurological disease. All participants performed the written SDMT, c-SDMT, and the Test of Non-verbal Intelligence Fourth Edition (TONI-4). Statistical analyses included a multivariate analysis of covariance (MANCOVA) for the effects of intelligence (IQ) and hospitalization on the performance of the SDMT and c-SDMT. A repeated measures analysis of variance (repeated measures ANOVA) was used to compare performance across c-SDMT trials between inpatients and outpatients. Results The MANCOVA showed that hospitalization had a significant effect on IPS when measured with the SDMT (p = 0.04) but not with the c-SDMT (p = 0.68), while IQ (p = 0.92) had no effect on IPS. Age (p < 0.001) was the best predictor of performance of both tests. The repeated measures ANOVA revealed no significant difference in within-test performance (p = 0.06) between outpatient and inpatient participants in the c-SDMT. Conclusion Performance of the c-SDMT is not confounded by hospitalization and gives within-test information. As a valid and reliable measure of IPS for children and adolescents, it is suitable for use in both inpatient and outpatient populations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".