Charles Bonnet syndrome: development and validation of a screening and multidimensional descriptive questionnaire
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to develop a French Canadian questionnaire for the detection of Charles Bonnet syndrome that allows for (i) valid screening and (ii) the examination of different dimensions of the client's visual hallucinations in order to better assess the resulting needs. METHOD: Questionnaire development was guided by interviews with visually impaired individuals experiencing visual hallucinations, as well as supported by scientific literature and expert experience. A clinical study involving 76 individuals with low vision was conducted to determine the sensitivity and specificity of the instrument according to criterion validation. RESULTS: Of the 54 closed-ended questions, a subset of 11 revealed a sensitivity of 1.00 and a specificity of 0.77. Two additional questions showed high discriminating potential. Improvements to the wording and structure of some questions aiming at needs assessment were identified and applied. The improved version consists of 55 questions grouped in 8 dimensions: (1) Screening; (2) Characteristics of hallucinations; (3) Psychological impact; (4) Psychopathological origin; (5) Coping strategies; (6) Context of appearance of hallucinations; (7) Time-related matters; (8) Psychosocial support. The screening is operationalized through an algorithm applied to the set of 13 questions. CONCLUSION: The questionnaire will be a valuable aid in screening for Charles Bonnet syndrome among the low vision clientele. However, the screening will need to be supplemented by a focused low vision interdisciplinary assessment including a visual examination and a clinical interview with a psychologist.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".