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Record W3179238984 · doi:10.1007/s13555-021-00572-2

The Proposed PASI-HD Provides More Precise Assessment of Plaque Psoriasis Severity in Anatomical Regions with a Low Area Score

2021· letter· en· W3179238984 on OpenAlex
Kim Papp, Mark Lebwohl, Leon Kircik, David M. Pariser, Bruce Strober, Gerald G. Krueger, David R. Berk, Lynn Navale, Robert Higham

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

VenueDermatology and Therapy · 2021
Typeletter
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsProbity Medical Research
FundersSanofi
KeywordsPsoriasis Area and Severity IndexBody surface areaPsoriasisPlaque psoriasisMedicineSeverity of illnessMeasure (data warehouse)DermatologyInternal medicineComputer scienceData mining

Abstract

fetched live from OpenAlex

The Psoriasis Area and Severity Index (PASI) is the most widely used clinical measure in clinical trials to assess disease severity of plaque psoriasis. However, the PASI is not a precise measure of severity with less precision when the regional area of involvement is < 10% of the BSA of a specific anatomical region. Degradation of precision results from the area score defaulting to '1' when the area of involvement within an anatomical region falls between 0% and 10% of the BSA for a given anatomical region. We describe a modification to the PASI, termed PASI-high discrimination (PASI-HD), for determination of more accurate psoriasis severity in body regions where < 10% of the body surface area is affected. The methodology for assessing disease severity in these conditions is described.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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