A Canadian Population-Based Cohort to the Study Cost and Burden of Surgically Resected Hidradenitis Suppurativa
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a chronic debilitating disease with long-lasting comorbidities that impose direct and indirect costs on the health care system. However, limited studies have estimated the burden of this disease in Canada, and no population-based studies have previously addressed this condition. OBJECTIVES: This work describes the characteristics of a population-based HS cohort to address the existing knowledge gap on the burden of HS for the Canadian health care system. This cohort will provide a foundation for further studies about clinical outcomes and risk factors of HS by providing opportunities for merging additional databases. METHODS: Data on demographic information, morbidities, relative resource use, and the cost of sectorial services were obtained from the Institute for Clinical Evaluative Sciences (ICES). All residents of Ontario covered by the Ontario Health Insurance Plan (OHIP) between April 1, 2002, and March 31, 2013, who underwent surgery for HS, defined by OHIP billing codes, were included. RESULTS: A total of 6244 cases were included in the analysis, following quality control procedures. Twice as many females were treated surgically relative to males. The majority of individuals treated were under the age of 64, with more than half having a moderate level of morbidity (according to Resource Utilization Bands defined by the Johns Hopkins Adjusted Clinical Group Classification System). CONCLUSIONS: This cohort study is the first population-based resource about HS in Canada. Administrative population-based databases provide essential information to assess the burden of chronic diseases and identify factors associated with higher cost.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".