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Record W3104545069 · doi:10.1097/der.0000000000000594

A Novel Device that Reduces Preparation Times and Precision Mass Error in Patch Testing for Allergic Contact Dermatitis

2020· article· en· W3104545069 on OpenAlexvenueno aff
Joseph Schanbacher, Peter C. Schalock

Bibliographic record

VenueDermatitis · 2020
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsAllergic contact dermatitisHaptenMedicinePatch testingPatch testContact dermatitisBiomedical engineeringDermatologyAllergyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Patch test preparation for evaluation of allergic contact dermatitis is traditionally a slow process with inherent errors. OBJECTIVE: A novel device, referred to as a syringer, designed to dispense 10 unique petroleum-based haptens simultaneously, significantly reduces preparation time and increases the precision of the mass dispensed per well. METHODS: The syringer was custom designed and "printed" through the use of a 3-dimensional printer with a polylactic acid plastic medium. RESULTS: The syringer dispensed 10 haptens significantly (P < 0.05) faster: 6.9 seconds on average, compared with 29.6 seconds by the traditional method. The syringer demonstrated a significantly (P < 0.05) lower average deviation of each strip's per-well mass average compared with the traditional method. CONCLUSIONS: In practice, this syringer is ideal for preparing patient-ready patch tests in quantities of 2 identical strips or more.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.300
Teacher spread0.244 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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