Practical Tips for Paediatricians: When is an eye turn more than just an eye turn
Bibliographic record
Abstract
Strabismus, or ocular misalignment, can affect up to 7% of children (1,2). Etiologies range from refractive errors to neurological deficits and systemic conditions. Strabismus can be classified as comitant or incomitant, depending on whether the deviation is equal or restricted in different directions of gaze, respectively, (1). Paralytic causes, which present as incomitant strabismus, result from weakness of the extraocular muscles due to cranial nerve palsies (1,3). Precise differentiation is crucial, given the need for imaging for incomitant strabismus and increased risks for vision loss, morbidity, and mortality (3,4). Clinicians should obtain a focused history including the onset, precipitating factors, and any neurological symptoms associated with the strabismus. In infants and younger children, the parental concern is usually around the cosmetic defect of the strabismus, as the child learns to suppress the image from the deviated eye and is often asymptomatic (1). Paralytic strabismus often manifests suddenly, while non-paralytic causes tend to have a more gradual progression (3). Precipitating factors such as head injury or systemic illness are red flags (1). Pain associated with strabismus could indicate a traumatic or inflammatory etiology. Diplopia (double vision), headaches, and nausea may indicate a neurological etiology (1).
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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 | 0.007 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.082 | 0.044 |
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".