Bibliographic record
Abstract
Background: The ORVIS infrastructure aims to facilitate the development and availability of valid and adapted tools that will allow functional, perceptual, cognitive and psychological evaluation of low vision clients by researchers and clinicians who work in low vision and visual impairment rehabilitation. Methods: The tools developed or documented within ORVIS Infrastructure are tests or questionnaires which allow, or will allow to assess—in an accurate and reliable manner—characteristics related to visual impairment. The tools in development are: (I) questionnaire de repérage des hallucinations visuelles liées au syndrome de Charles-Bonnet (QR-SCB); (II) repérage des personnes âgées présentant des INDices de déficience VISUELle (IndiVisuel); (III) mesure de l’impact de la déficience visuelle dans les activités quotidiennes (MIDVAQ) and (IV) M’EYE read test. The directory documents 14 tools and offers—within a descriptive sheet—characteristics, components and metrological properties as supported by cited scientific studies. Results: The ORVIS Infrastructure, which aims at the development and availability of assessment tools, fills researchers’ and clinicians’ needs for measurement tools that are valid, effective and appropriate for use with a visually impaired clientele. Such tools are, especially in French, little known and hard to find, and represent a precious resource for those who want to evaluate the efficacy of treatments or interventions. Conclusions: ORVIS is available at www.orvis.vision. Between November 2015 and September 2017, the directory has been accessed 1,383 times by 952 unique visitors.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.351 | 0.342 |
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".