Causes and incidental neuroimaging findings in pharmacologically confirmed Horner syndrome: a retrospective cohort study of 134 cases
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of structural causes in pharmacologically confirmed Horner syndrome (HS) and assess the clinical significance of incidental neuroimaging findings. DESIGN: A retrospective cohort study. PARTICIPANTS: A total of 134 patients with pharmacologically confirmed HS who underwent neuroimaging at a tertiary neuro-ophthalmology clinic between July 2018 and June 2024. METHODS: Neuroimaging findings were reviewed and categorized as either structural causes of HS or incidental. Incidental findings were further classified on the basis of their clinical relevance and need for follow-up. Cases were stratified by symptom duration into acute (<21 days), subacute (21 days to 3 months), and chronic (>3 months). RESULTS: A structural cause for HS was identified in 14.9% (n = 20) of patients, with higher detection rates in acute (26.0%) and subacute (21.4%) presentations compared to chronic cases (6.5%). The most common causes were internal carotid artery (ICA) dissection (3.0%) and ICA aneurysm (2.2%). Incidental findings were observed in 35.6% (n = 47) of patients, most commonly microangiopathic changes (10%), sinus/mucosal changes (3%), and thyroid nodules (3%). While 18.2% (n = 24) of patients required routine follow-up, only 1.5% (n = 2) needed urgent follow-up, and none required emergency intervention. CONCLUSIONS: Structural causes of HS are more frequently identified in acute and subacute presentations, supporting early imaging, especially to rule out ICA dissection. In chronic HS, selective imaging may be appropriate due to lower diagnostic yield. The high rate of incidental findings underscores the need for careful interpretation to optimize resource use and avoid unnecessary investigations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".