Exploring Access to Surgical Interventions for Hidradenitis Suppurativa: Retrospective Population-Based Analysis
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a painful inflammatory disorder that confers significant distress to patients, with surgery as an integral treatment modality. OBJECTIVE: To inform improvements in care, patterns in HS surgery were assessed. METHODS: A retrospective population-based analysis was performed on Ontario billing claims for HS surgery across a period of 10 years from January 1, 2008 to December 31, 2017. HS surgery was defined as the excision of inguinal, perineal, or axillary skin and sweat glands for hidradenitis. The top 5 billing specialties, including general and plastic surgery, were analyzed. The total number of procedures performed as well as the number performed per physician were investigated. Patient and physician locations were compared. RESULTS: A total of 7195 claims for the excision of inguinal, perineal, or axillary skin and sweat glands for HS were submitted across the study period. Annual HS surgery claims showed an increasing trend across 10 years, ranging between 4.9 and 5.8 per 100,000 population. However, overall, for every additional year, the number of claims per 100,000 population only increased slightly, by 0.03 claims. The number of providers steadily decreased, ranging between 1.7 and 1.9 per 100,000, with approximately twice as many general than plastic surgeons. However, again overall, for every additional year, the number of providers per 100,000 population decreased slightly, by 0.002 physicians. The mean annual number of procedures per physician rose from 2.8 to 3.1. In rural areas, analyzed per claim, general surgeons performed the majority of surgeries (1318/2003, 65.8%), while in urban areas, surgeries were more equally performed by general (2616/5192, 50.4%) and plastic (2495/5192, 48.1%) surgeons. Of HS surgery claims, 25.7%-35.9% were provided by a physician residing in a different area than the patient receiving care. CONCLUSIONS: No significant improvements in access to HS surgery were seen across the study period, with access potentially worsening with annual HS claims rising overall and number of providers decreasing, with patients travelling further to access surgery. System barriers across the continuum of HS diagnosis and management must be evaluated to improve access to surgical care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".