The Unified Visual Function Scale Assessments Show Inter- and Intraobserver Agreement and Correlate with Patient Quality of Life in Skull Base Parasellar Tumors
Bibliographic record
Abstract
Abstract Introduction The authors have previously described the Unified Visual Function Scale (UVFS). Here, we assessed intra- and interobserver reliability of the scale, and investigated correlations with patient quality of life (QoL). Methods Eight healthcare practitioners independently applied the UVFS in 20 representative cases from our parasellar meningioma series. Scoring was compared with consensus grades assigned by lead authors. Inter- and intraobserver agreement was measured using intraclass correlation coefficient (ICC), Fleiss's κ, and Cohen's κ, respectively. Patient QoL was assessed Visual Function Questionnaire 25 (VFQ-25) or Activities of Daily Vision Scale (ADVS), and correlated with UVFS grades for each eye. Results The interobserver ICC was 0.734 (95% confidence interval [CI]: 0.652–0.811), with Fleiss's κ of 0.758, 0.691, and 0.899 for grades A, B, and C, respectively. The intraobserver ICC was 0.758 (95% CI: 0.638–0.872), and Fleiss's κ was 0.604, 0.268, and 0.910 for grades A, B, and C respectively. The Cohen's κ for agreement between UVFS category grades and consensus grades was 0.816 (95 CI: 0.698–0.934). Survey response rate was 51% (27/53). The UVFS demonstrated strong correlation with VFQ-25 subdivisions general vision (r = 0.7712), near activities (r = 0.7262), peripheral vision (r = 0.6722), and driving (r = 0.6608), and also demonstrated strong correlation with the overall ADVS score (r = 0.5902). Conclusion This study shows that the UVFS is valid within a small subset of observers, and accurately reflects patient QoL. It is robust and practical, which make it suitable for broad implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".