Socioeconomic Status and Kawasaki Disease Outcomes in a Single-Payer Health Care System
Bibliographic record
Abstract
Background For patients with Kawasaki disease (KD), lower socioeconomic status (SES) may adversely affect the timeliness of presentation and initiation of intravenous immune globulin, and coronary artery outcomes. Multipayer systems have been shown to affect health care equity and access to health care negatively. We sought to determine the association of SES with KD outcomes in a single-payer health care system. Methods Patients with KD presenting from 2007 to 2017 at a single institution were included. SES data were obtained by matching patient postal code district with data from the 2016 Census Canada. Results SES data were linked for 1018 patients. The proportion of households living below the after-tax low-income cutoff in the patient's postal code district was 13% for not treated, 13% for delayed intravenous immune globulin treatment, and 12% for prompt treatment ( P = 0.58). Likewise, the average median annual household income was unrelated to delayed or no treatment. The percentage >15 years of age with advanced education differed between groups at 33%, 29%, and 31% for delayed treatment, prompt treatment, and missed groups, respectively ( P = 0.004). SES variables were not significantly different for those with vs without coronary artery aneurysms (max Z-score: >2.5), including the proportion of households living below low-income cutoff (12% vs 13%; P = 0.37), average median annual household income (CAD$81,220 vs $82,055; P = 0.78), and proportion with a university degree (33% vs 31%; P = 0.49), even after adjusting for sex, age, year, and KD type. Conclusions Timeliness of treatment for KD and coronary artery outcomes were not associated with SES variables within a single-payer health care system.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".