Hidradenitis suppurativa and Down syndrome: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS), characterized by inflammatory nodules, sinus tracts, and abscesses, has been linked to several factors, including immune dysfunction and obesity, which are thought to contribute to its development. Several follicular disorders have also been associated with Down syndrome (DS), a common chromosomal disorder, including HS, although studies on this topic are limited. OBJECTIVES: To characterize HS in Down syndrome patients and to further examine the association between HS and DS compared to HS patients without DS. METHODS: We systematically searched MEDLINE, Embase, Web of Science, and CENTRAL electronic databases from their dates of conception to February 2020. Random-effects meta-analyses were performed analyzing (a) HS characteristics between DS and non-DS participants, and (b) prevalence or association between HS and DS compared to non-DS individuals. RESULTS: Twelve studies were included in this systematic review, with a total of 358 participants presenting with both HS and DS. Pooled analysis of mean differences between DS and non-DS participants presenting with HS found a significantly younger age of HS symptom onset for DS patients (-6.24; 95% CI, -10.01--2.24). A meta-analysis examining the association between HS and DS found a significantly increased likelihood of HS in DS patients (OR 9.61; 95% CI, 5.70-16.20). CONCLUSIONS: Our findings suggest an association between HS and DS, with DS patients suffering from an earlier onset of HS symptoms compared to non-DS patients.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".