How to do conjunctival and buccal biopsies to investigate cicatrising conjunctivitis: improving the diagnosis of ocular mucous membrane pemphigoid
Bibliographic record
Abstract
A 79-year-old female presented with a 3-year history of trichiasis and red eyes (figure 1A). Multiple operations for entropion and trichiasis had been carried out previously, and a conjunctival biopsy investigating mucous membrane pemphigoid (MMP), had been negative. Her right eye was blind due to retinal detachment and glaucoma. Acuity was light perception in the right eye (OD) and 6/12 in the left eye (OS). There was active inflammation and bilateral inferior symblepharon, left upper lid entropion and trichiasis. There were no systemic features of MMP. Figure 1 (A) The patient's cicatrised inflamed left eye. (B) Taking a biopsy from the eye (not the same patient). If the speculum does not fit, manually open the lids. (C) Taking a buccal biopsy. (D) Michel's transport medium. (E) Direct immunofluorescence showing linear IgG, IgA along the basement membrane zone. Conjunctival and buccal mucosa biopsies were undertaken. Serum was tested for indirect inmunofluorescence. Conjunctival and serum immunofluorescence were negative, but buccal mucosa showed linear IgG and IgA staining along the basement membrane zone (BMZ), consistent with MMP. Oral prednisolone 1 mg/kg/day on a tapering course over 6 weeks, and mycophenolate 1 g twice daily were commenced. Nine months later, left upper lid entropion surgery was performed with excellent results. 1. What investigations are needed to identify the cause of cicatrising conjunctivitis? 2. Describe how to do conjunctival and buccal biopsies? 3. How would you manage the patient if you suspect ocular MMP, but all immunopathology …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".