Predictors of 2 year outcomes of juvenile idiopathic arthritis in a multicenter Canadian cohort: the ReACCh out experience
Bibliographic record
Abstract
Data was available on 223 to 291 children depending on the analysis. The 2-year clinical outcomes were remission, a Childhood Health Assessment questionnaire (CHAQ) score of ≥0.75, and the Juvenile Arthritis Quality of Life Questionnaire (JAQQ) score. Remission was defined according to Wallace criteria (without ESR or CRP), and combined patients on and off medication. The candidate predictors listed in Table 1 were selected for their clinical relevance and forced in a stepwise manner into regression models. They underwent natural logarithmic transformation (Ln) if not normally distributed. Logistic regression was used for prediction of remission and CHAQ outcomes, and linear regression for the JAQQ score. Table 1 shows results of the regression models. The only significant independent predictor of remission at both month 0 and 6 was the number of active joints, but R was low (0.06 and 0.09 at month 0 and month 6 respectively). A CHAQ score ≥0.75 was a strong independent predictor of a high 24-month CHAQ at month 0 but not at month 6 (R of 0.22 and 0.24 respectively). The JAQQ score at both month 0 and month 6 was the only significant independent predictor of the 24 month JAQQ score (R of 0.09 and 0.23 respectively). There was a non-statistically significant trend for rheumatoid factor positivity to be associated with lesser chance of remission and CHAQ score ≥0.75 at 24 months. As hypothesized, most measures at 6 months were better than the baseline measures at predicting 2 year outcomes. Interestingly, CHAQ score at baseline seemed a better predictor of 2 year outcome than CHAQ score at 6 months. Although only a single variable was a significant independent predictor of the outcomes in each model, all 3 models explained a larger proportion of variation in the outcome when the additional variables were included. These results highlight the relatively limited ability to predict disease course at early stage of disease, and should act as an impetus for further research into relevant clinical and biochemical markers of outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".