Development of a screening tool for reduced vision among inpatients of hospital rehabilitation units
Bibliographic record
Abstract
SIGNIFICANCE: This study developed a practical screening tool to identify reduced habitual vision (RHV) in hospital rehabilitation units. This tool would enhance patient care by enabling timely interventions in resource-limited settings. PURPOSE: The study aimed to develop a practical and implementable screening tool to identify patients with RHV in hospital rehabilitation units. Potential vision measures, screening questions, and demographic variables were considered to determine the optimum combination. METHODS: The cross-sectional study recruited 112 adult inpatients (aged 18+ years) from three rehabilitation units in an acute care hospital in Ontario, Canada, between October 2018 and February 2019. Data included an oral questionnaire on demographics, health status, and self-reported vision function, alongside vision assessments (distance visual acuity [VA], contrast sensitivity [CS], visual fields [VFs], and stereopsis). Univariate and multivariate logistic regression analyses were conducted to identify significant predictors of RHV, defined by VA >0.3 logMAR, CS <1.40 logCS, or any VF defect. RESULTS: The average age of participants was 74.5 years (±14.3 years), and RHV was present in 48.7%. Significant predictors of RHV included self-reported "happiness" with vision with current spectacles and difficulty reading a newspaper. The optimal predictive factors were VA and VF testing (96% sensitivity), but for practical implementation, the combination of three self-reported questions (happiness with vision, difficulty reading a newspaper, and difficulty distinguishing facial expressions) demonstrated 74% sensitivity. CONCLUSIONS: The study highlights that a combination of self-reported questions can effectively identify patients with RHV, providing a feasible alternative to direct vision assessments in resource-limited settings. Implementing this screening tool could improve patient care by enabling timely adaptations and referrals for eye care, ultimately enhancing rehabilitation outcomes and reducing falls risk. Further research is needed to refine the tool's sensitivity and explore its applicability in broader hospital and primary care settings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".