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Record W3111218213 · doi:10.1071/hc20092

Macroscopic and dermoscopic evaluation used to differentiate subungual haemorrhage from melanocytic lesions

2020· article· en· W3111218213 on OpenAlexaff
Mirain Phillips, Amanda Oakley

Bibliographic record

VenueJournal of Primary Health Care · 2020
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsSKiN Health
Fundersnot available
KeywordsNail (fastener)MedicineDermatologyMalignancyNail diseaseNail platePathologyParonychia

Abstract

fetched live from OpenAlex

INTRODUCTION Subungual haemorrhage describes blood located between the nail matrix and nail plate caused by trauma. Lack of recalled trauma and long duration of nail pigmentation results in specialist referrals to rule out malignant pathology. AIM This report aims to describe the macroscopic and dermoscopic characteristics of subungual haemorrhage and to highlight its clinical differentiation from melanocytic lesions. METHODS Ninety-eight nails were assessed. Pigmentation in fifty-nine was due to subungual haemorrhage and was melanocytic in the remainder (identified by a longitudinal pigmented band). RESULTS Pigmentation in subungual haemorrhage had a clear proximal margin (73%) and the dermoscopic pattern was homogenous (97%), globular (78%) or streaky (34%). Features included peripheral fading (68%) and periungual haemorrhage (5%). Malignancy could be excluded in these cases by careful clinical evaluation. DISCUSSION A combination of macroscopic and dermoscopic characteristics help make a confident diagnosis of subungual haemorrhage. A two-stage process can aid clinical diagnosis by looking for known features of subungual haemorrhage and identifying absence of malignant features.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

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

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 head0.056
GPT teacher head0.363
Teacher spread0.307 · 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 teacher head, 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

Citations5
Published2020
Admission routes1
Has abstractyes

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