Atopic dermatitis and skin infections are a poorly documented crisis in Canada's Indigenous pediatric population: It's time to start the conversation
Bibliographic record
Abstract
Canadian Indigenous youth continue to face higher rates of health disparities than their non-Indigenous counterparts. In dermatology, this includes a high burden of atopic dermatitis, as well as secondary skin and soft tissue infections. Unfortunately, numerous barriers to treatment exist, including systemic and institutional racism, poverty, crowded housing conditions on reserves, access and cost of basic skin care regimens, and clean water access. As per the Truth and Reconciliation Commission, Canadian dermatologists have been called upon to train more First Nations, Metis, and Inuit physicians to ensure we are providing high-quality, anti-racist, culturally appropriate care to Indigenous peoples.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".