Differentiating clinical characteristics of perianal inflammatory bowel disease from perianal hidradenitis suppurativa
Bibliographic record
Abstract
BACKGROUND: Perianal draining tunnels in hidradenitis suppurativa (HS) and perianal fistulizing inflammatory bowel disease (IBD) present diagnostic and management dilemmas. METHODS: We conducted a retrospective chart review of patients with perianal disease evaluated at Mayo Clinic from January 1, 1998, through July 31, 2021. Patients' demographic and clinical data were extracted, and 28 clinical features were collected. After experimenting with several machine learning techniques, random forests were used to select the 15 most important clinical features to construct the diagnostic prediction model to distinguish perianal HS from fistulizing perianal IBD. RESULTS: A total of 263 patients were included (98 with HS, 100 with IBD, and 65 with both IBD and HS). Patients with HS had a higher mean body mass index, a higher smoking rate, and more commonly showed cutaneous manifestations of tunnels and comedones, while fistulas, abscesses, induration, anal tags, ulcers, and anal fissures were more common in patients with IBD. In addition to having lesions in the perianal area, patients with IBD often had lesions in the buttocks and perineum, while those with HS had additional lesions in the axillae and groin. Among the statistically significant features, the 15 most important were identified by random forest: fistula, tunnel, digestive symptom, knife-cut ulcer, perineum, body mass index, age, axilla, abscess, tags, smoking, groin, genital cutaneous edema, erythema, and bilateral/unilateral. CONCLUSIONS: The results of this study may help differentiate perianal lesions, especially perineal HS and fistulizing perineal IBD, and provide promise for a better therapeutic outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".