Determining predictors of outcome on factors of att ention following paediatric arterial ischemic stroke
Bibliographic record
Abstract
Attention is a facet of cognition that is responsible for the development of most cognitive processes. Insult to the brain prior to or during the development of attention can be detrimental to various aspects of cognitive development and, as a result, to a child's ability to acquire new knowledge and skills. One example of cerebral insult in childhood is stroke. Given the importance of attention for the development of cognitive skills, identifying the factors of attention is critical to understanding cognitive outcomes in children with stroke. In the present investigation, a three-factor and a four-factor model of attention were tested using confirmatory factor analysis on a set of neuropsychological tests purported to measure various aspects of attention, in order to determine the model of attention best represented by a sample of children with arterial ischemic stroke. It was determined that both a three- and four-factor model of attention fit the data equally well when the same measures were included in both models. Despite similarities between the models, the four-factor model of attention was argued to be the best fit, due to theoretical, neuroanatomical, and developmental considerations. When the four-factor model was used to determine predictors of outcome, both Age at Stroke and Age at Testing were significant predictors of outcome on the Shift and Focus/Execute factors of attention, but not on the Encode and Sustain factors. The findings are discussed within the framework of a vulnerability vs. a plasticity model. Implications for clinical practice are also considered.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".