Montreal Brain Injury Vision Screening Test for General Practitioners
Bibliographic record
Abstract
Visual disturbances are amongst the most commonly reported symptoms after a traumatic brain injury (TBI) despite vision testing being uncommon at initial clinical evaluation. TBI patients consistently present a wide range of visual complaints, including photophobia, double vision, blurred vision, and loss of vision which can detrimentally affect reading abilities, postural balance, and mobility. In most cases, especially in rural areas, visual disturbances of TBI would have to be diagnosed and assessed by primary care physicians, who lack the specialized training of optometry. Given that TBI patients have a restricted set of visual concerns, an opportunity exists to develop a screening protocol for specialized evaluation by optometrists-one that a primary care physician could comfortably carry out and do so in a short time. Here, we designed a quick screening protocol that assesses the presence of core visual symptoms present post-TBI. The MOBIVIS (Montreal Brain Injury Vision Screening) protocol takes on average 5 min to perform and is composed of only "high-yield" tests that could be performed in the context of a primary care practice and questions most likely to reveal symptoms needing further vision care management. The composition of our proposed protocol and questionnaire are explained and discussed in light of existing protocols. Its potential impact and ability to shape a better collaboration and an integrative approach in the management of mild TBI (mTBI) patients is also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".