Modeling of Longitudinal Academic Achievement Scores After Pediatric Traumatic Brain Injury
Bibliographic record
Abstract
In a prospective longitudinal study, academic achievement scores were obtained from youth 5 to 15 years of age who sustained mild-moderate (n = 34) or severe (n = 43) traumatic brain injuries (TBI). Achievement scores were collected from baseline to 5 years following TBI and were subjected to individual growth curve analysis. The models fitted age at injury, years since injury, duration of impaired consciousness, and interaction effects to Reading Decoding, Reading Comprehension, Spelling, and Arithmetic standard scores. Although scores improved significantly over the follow-up relative to normative data from the standardization sample of the tests, children with severe TBI showed persistent deficits on all achievement scores in comparison to children with mild-moderate TBI. Interactions of the slope and age parameters for the Arithmetic and Reading Decoding scores indicated greater increases over time in achievement scores of the children injured at an older age, but deceleration in growth curves for the younger children with both mild-moderate and severe TBI. These results are compatible with the hypothesis that early brain injuries disrupt the acquisition of some academic skills. Hierarchical regression models revealed that indexes of academic achievement obtained 2 years following TBI had weak relations with the duration of impaired consciousness and socioeconomic status. In contrast, concurrent cognitive variables such as phonological processing and verbal memory accounted for more variability in academic scores. Given the significant and persistent decrement in basic academic skills in youth with severe TBI, it is clear that head-injured youth require intensive, long-term remediation and intervention not only of the academic skills themselves, but also of those cognitive abilities that support the development and maintenance of reading and math.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".