Development and validation of Visual Impairment as a Risk for Falls Questionnaire
Bibliographic record
Abstract
Purpose: visual impairment is associated with an increased risk of falls, yet eye care professionals are infrequently members of falls prevention clinics. The aim of this preliminary study was to validate a newly created Visual Impairment as a Risk for Falls Questionnaire intended to be used by those professionals not involved in eye care. Methods: about 53 participants with various visual impairments known to be associated with falls and 33 participants with normal sight were contacted within 4 months of a full oculo-visual assessment and were asked the questions from the current questionnaire pertaining to their visual function. A retrospective file review was undertaken and the sensitivity and specificity of participants' responses were calculated compared to the actual vision impairment based on the findings from their visual assessment. Results: the question regarding ability to read was included to identify people with central vision loss, a risk factor for falling. It was found to have sensitivity of 74% and specificity of 87%. Both sensitivity and specificity improved when participants with cognitive impairment were excluded. The question on recognising facial features gave sensitivity of 73% and specificity of 97% for this subgroup. However, questions related to impairments in stereopsis and peripheral fields were not useful. Conclusion: the study demonstrates that several questions of the new questionnaire are useful; however, further testing with a larger population is needed to fully validate the questionnaire for use by health care professionals.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".