131: Health and Quality of Life Outcomes for a Cohort of Children with Severe Traumatic Brain Injury
Bibliographic record
Abstract
The Multiattribute Health Status Classification System (MHSCS) is a parent/proxy administered tool including a series of health-specific domains that give levels of functioning between 1 (normal health) to 4 or 5 (most severe). Previous studies have used the MHSCS to document chronic health impairment and child morbidity (Gemke et al., 1995; 1996; Feeny et al., 1992; 1993; Saigal et al., 1994). There is a high inter-rater reliability exceeding 70% between caregivers and physiatrists with sensitivity and specificity from acute injury predictors at 75% and 70% (Robertson et al., 2001). The MHSCS is a useful parent-report surveillance tool to audit outcome after severe Traumatic Brain Injury (TBI). To assess the post-injury health outcomes of young children sustaining a severe TBI using the MHSCS. This is a retrospective chart review of children who sustained severe TBI before 12 years of age & were followed in the Brain Injury program. Severe TBI was defined as Glasgow Coma Scale =<8/15. Ethics approval was granted. Demographic variables were collected and expressed as mean (± SD) (range). A multivariate analysis of variance was conducted to compare scores on the MHSCS. A total of 18 children (10 male, eight female) mean age 6.84±3.4 years, (range 1.5 to 11.8 years) were reviewed at three years (n=8) or ≥5 years (n=10) post-injury. An independent t test indicated no significant difference between the Total scores at three and ≥5 years (P=0.23) (Chart 1). The overall difference across subscales was not significantly different (Chart 2). The Cognition Subscale scores increased significantly between three and ≥5 years (P=0.038) and the Emotional Subscale scores increased marginally (P=0.073). Outcomes of severe TBI persist beyond the acute recovery stages. Children experience significantly more difficulties with cognition and emotion at ≥5 years post injury than at three years. This may reflect emerging deficits as they face more complex material at school. Children also experience marginally more emotional difficulties at ≥5 years than at three years. This may reflect developmental maturity making them more aware of their TBI-related limitations. Although it was not significantly different, there was a trend towards lower scores for Mobility and Self-Care Subscales. The MHSCS was a useful parent-report surveillance tool to audit outcome after severe TBI.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".