A Novel Scale for Describing Visual Outcomes in Patients Following Resection of Lesions Affecting the Optic Apparatus-unified Visual Function Scale
Bibliographic record
Abstract
Introduction: Historically, description of patient visual acuity and visual field changes following intracranial procedures has been very rudimentary. Clinicians and researchers have relied on the use of basic descriptions such as “improved,” “worsened,” and “unchanged” to describe outcomes following resections of tumors affecting the optic apparatus. These descriptors are vague, difficult to quantify, and are challenging to apply in a clinical perspective. Several groups have attempted to combine visual acuity and visual fields into a single assessment score, but are not user-friendly. We present a novel way to describe a patient’s visual function as a combination of visual acuity and visual field assessment that is simple to use and can be used by surgeons, and researchers to gauge visual outcomes following tumor resection. Methods: The Unified Visual Function Scale is designed to include clinically relevant outcome factors affecting patients, as well as being sensitive enough to capture visual changes in a quantitative manner. We combine visual acuity and visual fields into three categories designed around the definition of legal blindness and fitness to drive in Canada. We then applied the scale to our previously published case series of 53 patients with perisellar meningiomas to test sensitivity and specificity for assessment of overall visual outcomes for patients undergoing craniotomy for tumor resection. We then compared the results against previously documented visual loss scales in the literature. Results: While previously designed visual outcome scales provide a detailed assessment of visual change following surgery, are unfortunately not practical for daily clinic use by clinicians. An ideal scale would be quick to apply while being descriptive using practical factors. With our scale we were able to capture the overall visual change while being sensitive enough to define the overall quantity of improvement or worsening quantitatively, using categories that rare clinically relevant and understandable. Conclusion: The Unified Visual Function Scale is a robust way to assess a patient’s vision combining visual fields and acuity. The implementation of pre and postoperative assessment is both sensitive enough to assess overall change, while providing clinically relevant information for surgeons, and allows for comparisons between treatment groups.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".