Clinical applications of multiple scaling theory: Focus on the big picture
Bibliographic record
Abstract
Patients suffering of foveal vision loss (e.g. macular degeneration) must rely on peripheral vision to perform most visual tasks. Often, this is insufficient, and corrective measures are necessary: (1) magnification, i.e. enlarging the stimulus (e.g. moving closer to the stimulus, using magnifying lens), and/or (2) stimulus optimization (e.g. controlling the spacing between letters, using a font optimized for peripheral vision). The current focus in peripheral vision research in normal populations is to measure, at each eccentricity, the smallest stimuli that can maintain threshold performance (i.e. lower limit). However, using multiple scaling theory (MST; Poirier & Gurnsey, 2002, 2005), we argue that this information is incomplete. Researchers also need to measure, at each eccentricity, the largest stimuli that can maintain threshold performance (i.e. upper limit). Upper limits are not uncommon in perception: (1) texture discrimination has an upper limit, where texture discrimination is impaired if textels on either side of a texture edge are spaced too far apart, and (2) reading has an upper bound, where normal scanning and reading span functions are disrupted when letters are too spaced apart. The relationship between these two limits determines if the task can be solved peripherally, and if so, what corrective measures are required. We will also review various sampling strategies used in peripheral vision research, and for each provide concrete ways to detect lower and upper limits. We discuss several applications of this research, including (1) guidelines for generalizing results from normal to clinical populations and vice-versa, (2) guidelines for generalizing results from incomplete data sets or sub-optimal sampling strategies, and (3) guidelines for identifying the proper corrective measures. These guidelines could play an essential role in bridging peripheral vision research across clinical and normal populations.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".