A novel scale for describing visual outcomes in patients following resection of lesions affecting the optic apparatus: the Unified Visual Function Scale
Bibliographic record
Abstract
OBJECTIVEHistorically, descriptions of visual acuity and visual field change following intracranial procedures have been very rudimentary. Clinicians and researchers have often used 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 challenging to apply in a clinical perspective. Several groups have attempted to combine visual acuity and visual fields into a single assessment score, but these are not user-friendly. The authors 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.METHODSVisual acuity and visual fields were combined into 3 categories designed around the definitions of legal blindness and fitness to drive in Canada. The authors then applied the scale (the Unified Visual Function Scale, or UVFS) to their previously published case series of perisellar meningiomas to document and test overall visual outcomes for patients undergoing tumor resection. The results were compared with previously documented visual loss scales in the literature.RESULTSUsing the UVFS the authors were able to capture the overall visual change; the scale was sensitive enough to define the overall visual improvement or worsening quantitatively, using categories that are clinically relevant and understandable.CONCLUSIONSThe UVFS is a robust way to assess a patient's vision, combining visual fields and acuity. The implementation of pre- and postoperative assessment is sensitive enough to assess overall change while also 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".