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Record W4412634825 · doi:10.2196/72773

Online Resources for Hidradenitis Suppurativa for Patient Use: Systematic Search and Analysis

2025· article· en· W4412634825 on OpenAlexvenueno aff
Emily Sheetz, Aryn A. Alanizi, Joshua Edwards, Alice Roberts

Bibliographic record

VenueJMIR Dermatology · 2025
Typearticle
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHidradenitis suppurativaPreprintMedicineDermatologyComputer scienceWorld Wide WebPathologyDisease

Abstract

fetched live from OpenAlex

Unlabelled: This research letter evaluates the quality and readability of hidradenitis suppurativa (HS) websites found on Google and Bing with the DISCERN instrument and Flesch-Kincaid Readability metrics. Comprehensive and reliable articles can lead to increased knowledge about HS and further enhance physician-patient relationships and shared decision-making. This study's aim was to identify reliable resources to help bridge knowledge gaps and support informed discussions on management and treatment options.

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)0.012
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.064
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0240.022
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.023
GPT teacher head0.336
Teacher spread0.313 · 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 designSystematic review
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
Published2025
Admission routes1
Has abstractyes

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