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Record W4280493361 · doi:10.1111/exd.14609

New treatments and new assessment instruments for Hidradenitis suppurativa

2022· review· en· W4280493361 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExperimental Dermatology · 2022
Typereview
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsHidradenitis suppurativaMedicineQuality of life (healthcare)Clinical trialDermatology Life Quality IndexReliability (semiconductor)Patient-reported outcomePhysical therapyClinical PracticeScale (ratio)Rating scaleMedical physicsStatisticsPathology

Abstract

fetched live from OpenAlex

Research interest in Hidradenitis Suppurativa (HS) has grown exponentially over the past decades. Several groups have worked to develop novel scores that address the drawbacks of existing investigator-assessed and patient-reported outcome measures currently used in HS trials, clinical practice and research. In clinical trial settings, the drawbacks of the HiSCR have become apparent; mainly, it is lack of a dynamic measurement of draining tunnels. The newly developed (dichotomous) IHS4 and HASI-R are backed up by adequate validation data and are good contenders to become the new primary outcome measure in HS clinical trials. Patient-reported outcomes, as well as physician reported measures, are being developed by the HIdradenitis SuppuraTiva cORe outcomes set International Collaboration (HISTORIC). For example, the Hidradenitis Suppurativa Quality of Life (HiSQOL) score is a validated measure of HS-specific quality of life and is already being used in many HS trials. Magnitude of pain measurement via a 0-10 numerical rating scale is well-established; however, consensus is still required to ensure consistent administration and interpretation of the instrument. A longitudinal measurement over multiple days rather than at one time point, such as for example the Pain Index could provide increased reliability and reduced recall bias. Ultimately, these newly developed scores and tools can be included in a standardized registry to be used in routine clinical practice.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.099
GPT teacher head0.420
Teacher spread0.321 · 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