Challenging Diagnosis of a Rare Case of Spontaneous Keloid Scar
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".