Melanoma-Associated Retinopathy Report of a Case and Review
Bibliographic record
Abstract
taking, physical examination and investigations.Precipitating events such as trauma, recent surgery, or infection should be noted.Associated signs and symptoms can narrow the differential.Multiple cranial nerve deficits, incoordination or nystagmus may indicate a structural lesion or vascular cause.Systemic sequelae of inflammatory or metabolic conditions can raise suspicion of rheumatoid arthritis, ankylosing spondylitis or diabetes mellitus.The investigative work-up should begin with a high resolution MRI as structural lesions are the most common cause.Magnetic resonance angiography can reveal vertebral or carotid artery dissection or ectasia.If inflammatory or metabolic causes are suspected, serologic testing for autoimmune disease or routine biochemistry can be ordered.A positive Monospot or elevated EBV titers can identify infectious mononucleosis.If after thorough investigation no clear etiology of the hypoglossal palsy can be found, then the possibility of isolated idiopathic hypoglossal nerve palsy remains.Infectious mononucleosis is a rare cause of hypoglossal nerve palsy but should be suspected in isolated, unilateral clinical presentations.There is no accepted management of postinfectious hypoglossal nerve palsy due to its rarity.Of the six cases reported, corticosteroid therapy was initiated in three cases.Five patients resolved completely(4); only one case resulted in a persistent deficit after pulse steroids(5).Based on the limited literature available, hypoglossal nerve injury postmononucleosis infection appears to be a benign self-limiting condition.The few case reports available suggest that once the more serious potential etiologies have been excluded, no further treatment is required.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".