Language performance within three months of early childhood traumatic brain injury
Bibliographic record
Abstract
PURPOSE: To examine language outcomes in the short-term stage (i.e., within three months) of early childhood traumatic brain injury (TBI). METHODS: A retrospective chart review over a 10-year period (January 1, 2007 to December 31, 2016) was completed at a single-site inpatient rehabilitation hospital. Inclusion criteria were children aged 15 months to five years 11 months with a diagnosis of closed TBI. RESULTS: Twenty-four charts were included in the descriptive analysis of language; there were fewer children with expressive language scores (n = 18) than receptive language scores (n = 24), likely due to word retrieval difficulties as per clinical documentation. Effects of TBI on language performance were more pronounced in receptive than expressive language. For children with scores in both receptive and expressive language areas (n = 18), five children had below average scores. These children were described as having language delays pre-injury (n = 2), lower exposure to English (n = 1), information processing difficulties (n = 1), and difficulties with formulation and organization of language (n = 1). CONCLUSION: This study represents an initial step in understanding expressive and receptive language performance shortly after early childhood TBI. Challenges with assessment as well as directions for future research are discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".