Comparison of Literal, Inferential, and Intentional Text Comprehension in Children with Mild or Severe Closed-Head Injury
Bibliographic record
Abstract
BACKGROUND: Children with head injury have impairments in pragmatic language at the level of both single words and texts. Text comprehension deficits are likely to be the more consequential for everyday and academic function, yet the relative magnitudes of literal and nonliteral text comprehension deficits have not been measured. DESIGN: We compared the magnitude of the impairment in three forms of text comprehension for children with mild or severe head injury relative with controls: literal language (understanding literal text information), inferential language (making pragmatic inferences, textual coherence inferences, or enriching inferences), and the language of mental states and intentions (eg, producing speech acts, appreciating irony, and understanding deception). MEASURES: Effect sizes were used to measure the magnitude of the difference between children with head injury and age-matched controls. RESULTS: Children with severe closed-head injury were significantly impaired on tasks of literal text understanding, inferencing, and intentionality. Children with mild head injury were impaired on some inferencing and all intentionality tasks, although they had no literal text comprehension deficits. CONCLUSIONS: For both groups, the greatest deficits (ie, the largest effect sizes) were on tasks requiring understanding of the language of mental states and intentions. The data bear on the long-term effects of childhood closed-head injury on text- and discourse-level language and also on the nature and timing of language rehabilitation in children with head 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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".