TORCH: Toronto Outcome Research in Child Health - enhancing evidence based outcomes selection in pediatric research
Bibliographic record
Abstract
Children’s responses to medical treatments differ significantly from adults. Appropriately selecting and measuring child and family relevant outcomes when designing pediatric clinical trials is important for decision making with regards to the health of the child. However, outcomes used to measure an intervention’s effectiveness in current pediatric clinical trials often lack child and family relevance, are heterogeneous across and within child health ages and diseases, and are not adequately measured with validated instruments. Furthermore, involvement of patients (children) and their proxies (usually parents) in outcomes selection is minimal. Inconsistent use of outcomes and outcomes measurement in pediatric clinical trials impairs the synthesis of evidence in systematic reviews and leads to outcome reporting bias. This high variability in outcome selection and measurement has led to a situation where child health decisions on treatment of children lack the appropriate underpinning evidence, and a subsequent inability to reach a consensus on the effectiveness and safety of a treatment. Recently, outcome selection initiatives in the general population such as OMERACT and COMET advocate homogeneity of methodology for outcome selection and measurement in trials. Toronto Outcome Research in Child Health (TORCH) is an exciting new collaborative initiative that develops and employs existing and new evidence-based methods for improving outcomes selection and measurement in cohort studies and trials in children.
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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 | 0.250 | 0.507 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.072 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".