Do Patterns of Early Disease Severity Predict Grade 12 Academic Achievement in Youths With Childhood-Onset Chronic Rheumatic Diseases?
Bibliographic record
Abstract
OBJECTIVE: To test the association of early disease severity with grade 12 standards test performance in individuals with childhood-onset chronic rheumatic diseases (ChildCRDs), including juvenile arthritis and systemic autoimmune rheumatic diseases. METHODS: We used linked provincial administrative data to identify patients with ChildCRDs born between 1979 and 1998 in Manitoba, Canada. Primary outcomes were Language and Arts Achievement Index (LAI) scores and Math Achievement Index (MAI) scores from grade 12 standards test results as well as enrollment data. The secondary outcome was enrollment in grade 12 by 17 years of age. Latent class trajectory analysis identified disease severity groups using physician visits following diagnosis. Multivariable linear regression tested the association of disease severity groups with LAI and MAI scores, and logistic regression tested the association of disease severity with age-appropriate enrollment, after adjusting for sociodemographic factors and psychiatric morbidities. RESULTS: The study cohort included 541 patients, 70.1% of whom were female. A 3-class trajectory model provided the best fit; it classified 9.7% of patients as having severe disease, 54.5% as having moderate disease, and 35.8% as having mild disease. After covariate adjustment, severe disease was associated with poorer LAI and MAI scores but not with age-appropriate enrollment. CONCLUSION: Among patients with ChildCRDs, those with severe disease performed more poorly on grade 12 standards tests, independent of sociodemographic and psychiatric risk factors. Clinicians should work with educators and policy makers to advocate for supports to improve educational outcomes of patients with ChildCRDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".