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Record W4308196115 · doi:10.21203/rs.3.rs-2228133/v1

Anemia in patients with hidradenitis suppurativa

2022· preprint· en· W4308196115 on OpenAlexaboutno aff
Rishab Revankar, Mary Rojas, Samantha Walsh, Heli Patel, Nikita Revankar, Joseph Han

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHidradenitis suppurativaAnemiaObservational studyOdds ratioData extractionCochrane LibraryMEDLINEMeta-analysisRandomized controlled trialConfidence intervalInternal medicineDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.043
GPT teacher head0.381
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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