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Record W2031884136 · doi:10.14740/jmc.v5i8.1887

Challenging Diagnosis of a Rare Case of Spontaneous Keloid Scar

2014· article· en· W2031884136 on OpenAlexvenueno aff
William H. C. Tiong, Normala Hj Basiron

Bibliographic record

VenueJournal of Medical Cases · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsKeloidMedicineScarsDermatologyDermisReticular DermisHypertrophic scarScar tissuePathognomonicSurgeryPathologyDisease

Abstract

fetched live from OpenAlex

Keloid scars are benign dermal collagenous lesions that arise in the reticular layer of the dermis as a result of a period of prolonged wound healing due to injurious cutaneous insult. It is diagnosed clinically by a history of injury preceding its appearance and its pathognomonic encroachment beyond the boundary of the original wound. There are multiple factors that predispose an individual to keloid scar formation but no single hypothesis sufficiently explained its complex pathogenesis.  The spontaneity of keloid scar to arise de novo without prior cutaneous injury is very rare. Although the non-specific appearance of spontaneous keloid scar can resemble various malignant or benign tumors, it is of paramount importance to accurately diagnose it due to the difficulty and complexity of keloid scar treatment or the atrocious outcome of its mismanagement. Here, we presented a rare case of spontaneous keloid scar over an unusual site of occurrence, and its disfiguring consequences as a result of misdiagnosis and treatment. We also highlighted the diagnostic challenge facing many clinicians when spontaneous keloid scar occurred in an individual without significant predisposing factors. Therefore, extra vigilance should be exercised in our practice and the spontaneity origin of keloid scar should be borne in one’s mind. J Med Cases. 2014;5(8):466-469 doi: http://dx.doi.org/10.14740/jmc1887w

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.038
GPT teacher head0.351
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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
Published2014
Admission routes1
Has abstractyes

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