Screening Scale of Oculomotor Symptoms in Traumatic Brain Injury Patients to Facilitate Referrals to Neuro-Ophthalmologists for Rehabilitation
Bibliographic record
Abstract
Background: Oculomotor dysfunction (OMD) is observed in perhaps 10% to 30% of motorists injured in high impact car accidents.A screening tool for signs of OMD is needed to facilitate systematic evaluations of such patients for potential referral to neuro-ophthalmologists for rehabilitation.Method: A perusal of neuro-opthalmological case reports and OMD literature led to the development of a 15 item scale.This Oculomotor Scale was administered to 29 survivors of high impact motor vehicle accidents (MVAs) who complained about OMD signs and to 30 normal controls.The patients' scores were also available on the Rivermead measure of the post-concussion syndrome, the Post-MVA Neurological Symptoms (PMNS) scale, the Insomnia Severity Index, and ratings of pain, depression, and anxiety.Results: Criterion validity of the Oculomotor Scale is demonstrated by its very satisfactory capacity to differentiate the patients reporting OMD signs from the normal controls (point biserial coefficient =.87, p<.001).Convergent validity of the scale is shown by its significant and large correlations to Rivermead post-concussive scores (r=.70, p<.001) and to the PMNS measure of post-accident neuropsychological symptoms (r=.65, p<.001). Discussion and Conclusions:The Oculomotor Scale has not been designed for independent diagnosing of oculomotor dysfunction (OMD): it is intended only as a brief screening scale for family physicians or medical psychologists, in order to facilitate a referral for the thorough professional assessment by specialized rehabilitative neuro-ophthalmologists.Hopefully, the availability of this screening scale would increase the number of referrals of such patients with post-accident oculomotor dysfunction for beneficial specialized assessment and therapies by rehabilitative neuro-ophthalmologists.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".