Localised hypopigmentation: clarification of a diagnostic conundrum
Bibliographic record
Abstract
Various forms of skin hypopigmentation can occur spontaneously. When multiple forms of hypopigmentation occur simultaneously, the diagnoses may seem unclear. This article illustrates a patient who presented simultaneously with each of vitiligo, (idiopathic) guttate hypomelanosis (IGH), and a rarely noted hypopigmented variant of seborrhoeic keratosis. We outline distinguishing clinical features for clinicians to consider on encountering a patient with adult-acquired hypopigmentation. Subsequently, we present a useful approach to diagnosing common acquired forms of localised hypopigmentation seen in primary care. A 61-year-old black female presented to her GP with white patches on her back (Figure 1a), and was diagnosed with vitiligo. Management included tacrolimus 0.1% ointment for daily use and referral to a dermatologist. She was also advised to avoid sun exposure to the affected areas. In the interim, the patient was exposed to ultraviolet radiation (UVR) while vacationing in Jamaica and noted improvement in the white patches. At her first dermatology visit, she displayed repigmenting patches of vitiligo with brown macules perifollicularly (Figure 1b). She queried whether new hypopigmented lesions were also vitiligo. Specifically, her back had light ‘stuck on’ papules and her arms and back had other 5-mm hypopigmented macules (Figure 1c and 1d). She queried why select back lesions improved after sun exposure despite being advised to avoid it. Her dermatologist explained that her initial back patches of vitiligo had repigmented due to UVR from …
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".