Topographical orientation as a model of plasticity in children with perinatal stroke
Bibliographic record
Abstract
Background: Children with perinatal stroke go on to develop most cognitive skills (e.g. language) due to brain plasticity; however, their performance is usually poor when compared to age-matched controls, indicating a reduced potential compared to uninjured children. To date, how plasticity after early injury affects the development of complex cognitive skills remains uncertain. Here, we use topographical orientation, which relies on integration of several cognitive processes underlain by widespread neural networks, as a model to test plasticity in complex behaviour. Methods: Children with perinatal stroke and age-matched controls were tested with a neuropsychological battery and a novel navigation task. In addition, for each patient, we obtained the most recent MRI scan to assess the effects of lesion characteristics on performance at the navigational task. Results: Children with history of injury performed worse than controls, and their scores were not different based on lesion’s laterality, location or functional region affected. In particular, involvement of regions known to contribute to spatial orientation did not result in significantly decreased performance. Conclusions: As seen in other skills, orientation was preserved, but decreased when compared to age-matched controls. Given its cognitive and neural complexity, topographical orientation may be used as a model for network plasticity after early injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".