Development of a case definition for hearing loss in community-based older adults: a cross-sectional validation study
Bibliographic record
Abstract
<h3>Background:</h3> Research based in primary care suggests that hearing loss may be underreported as well as inconsistently recorded in patient histories. In this study, we aimed to develop and validate a case definition for hearing loss among older adults in primary care, using electronic medical records. <h3>Methods:</h3> We used data from adult patients aged 55 years and older from 13 practices in the Southern Alberta Primary Care Research Network database, part of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), from Dec. 1, 2014, to Dec. 31, 2016. We developed a hearing loss case definition that was translated into an electronic algorithm. A record review was undertaken as the reference standard, followed by application of the algorithm to the sample. Validation metrics included sensitivity, specificity, positive predictive value and negative predictive value, as well as prevalence. We assessed risk factors using the Fisher exact test and odds ratios. <h3>Results:</h3> The sample included 1000 patients; 496 (49.6%) were female and the mean age was 67.5 (standard deviation 9.6) years. Sensitivity of the case definition algorithm was determined to be 87.3% (95% confidence interval [CI] 76.5%–94.4%) with specificity valued at 94.8% (95% CI 93.1%–96.1%). Positive and negative predictive values were 52.9% (95% CI 42.8%–62.8%) and 99.1% (95% CI 98.2%–99.6%), respectively. The prevalence of hearing loss within the sample was 6.3% (95% CI 4.9%–7.9%). Older age was a significant risk factor for hearing loss (<i>t</i> = 4.98, 95% CI 3.76–8.65). Men had greater odds of hearing loss than women (odds ratio 1.65, 95% CI 0.98–2.79). <h3>Interpretation:</h3> The validated case definition for hearing loss in community-based older adults had high sensitivity and specificity. It may be applied to surveillance and future epidemiologic research within the CPCSSN database.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".