Anemia in patients with hidradenitis suppurativa
Bibliographic record
Abstract
Abstract IMPORTANCE Hidradenitis suppurativa (HS) is associated with a number of physical and psychological comorbidities. Studies have suggested an association between HS and anemia; however, this association is not widely understood and may result in delayed diagnosis and treatment and possible increase in morbidity and mortality. OBJECTIVE To systematically review and perform a metanalysis regarding the association between HS and anemia, and to characterize the subtypes of anemia associated with HS. DATA SOURCES A search of the EMBASE, Medline, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials databases from the time of database inception to September 25, 2022, yielded 313 unique articles. STUDY SELECTION All observational studies and randomized controlled trials published in English that examined the odds ratio (OR) of anemia in patients with HS were screened by 2 independent reviewers. Case reports were excluded. Among 313 unique articles, 7 were deemed eligible. DATA EXTRACTION AND SYNTHESIS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines facilitated data extraction. The Newcastle-Ottawa Scale (NOS) was used to analyze risk of bias of included studies. In addition to OR and 95% confidence intervals, relevant data on patient demographics and anemia subtypes were also extracted. MAIN OUTCOMES AND MEASURES The primary outcome was the OR of anemia in HS patients. This study also attempted to characterize anemia subtypes associated with HS. RESULTS In total, 2 case-control and 5 cross-sectional studies featured a total of 11,693 patients. Among the studies, 4 of 7 demonstrated a statistically significant positive association between anemia and HS (ORs, 2.20 [1.42 to 3.41], 2.33 [1.99 to 2.73], 1.87 [1.02 to 3.44], and 1.50 [1.43 to 1.57]), with macrocytic and microcytic subtypes being most common. After adjusting for publication bias, meta-analysis with random effects revealed HS to be associated with increased odds of anemia compared to non-HS groups (OR 1.59 [1.19, 2.11]) CONCLUSIONS AND RELEVANCE There is a statistically significant positive association between HS and anemia, particularly for the aforementioned subtypes. Patients with HS should be screened for anemia. In case of lower hemoglobin concentration, the anemia of HS patients should be subdivided according to mean corpuscular volume of the red blood cells and further investigated depending on subtype.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".