Evaluation of how well different pure-tone threshold and visual acuity measures reflect self-reported sensory ability and treatment uptake: An analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
In epidemiology studies, researchers may choose to use summary scores of hearing or visionbased on self-report or clinical measures. Self-report and clinical measures may yield differentclassifications. Of the possible clinical measures, there is no consensus regarding which puretonethreshold average (PTA) or visual acuity (VA) measures are optimal. We aimed todetermine how well different PTAs and VA measures predicted self-reported measures ofsensory function. A cross-sectional analysis of 30,097 Canadians aged 45-85 years participatingin wave 1 of the Canadian Longitudinal Study on Aging in 2012-2015 was performed. Wecalculated the area under the receiver operating characteristic curves (AUC) for 9 different PTAsand 6 different VA measures. In the analysis of the PTAs, the classifiers used as comparatorsincluded self-reports of hearing and hearing aid use. In the analysis of the VA measures,comparators were self-reports of vision, and corrective lens use. The top-ranked PTA was thebinaural mid-frequency PTA (i.e., the average of hearing thresholds at 1000, 2000, 3000 and4000 Hz in both ears). The top-ranked VA measure was the average pinhole-corrected VA inboth eyes. These measures are not commonly used, but should be considered for epidemiologicalresearch.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".