Effective Hearing Loss Screening in Primary Care: The Early Auditory Referral-Primary Care Study
Bibliographic record
Abstract
<h3>PURPOSE</h3> Hearing loss, the second most common disability in the United States, is under-diagnosed and under-treated. Identifying it in early stages could prevent its known substantial adverse outcomes. <h3>METHODS</h3> A multiple baseline design was implemented to assess a screening paradigm for identifying and referring patients aged ≥55 years with hearing loss at 10 family medicine clinics in 2 health systems. Patients completed a consent form and the Hearing Handicap Inventory for the Elderly (HHI). An electronic alert prompted clinicians to screen for hearing loss during visits. <h3>RESULTS</h3> The 14,877 eligible patients during the study period had 36,701 encounters. Referral rates in the family medicine clinics increased from a baseline rate of 3.2% to 14.4% in 1 health system and from a baseline rate of 0.7% to 4.7% in the other. A general medicine comparison group showed referral rate increase from the 3.0% baseline rate to 3.3%. Of the 5,883 study patients who completed the HHI 25.2% (n=1,484) had HHI scores suggestive of hearing loss; those patients had higher referral rates, 28% vs 9.2% (<i>P</i> <.001). Of 1,660 patients referred for hearing testing, 717 had audiology data available for analysis: 669 (93.3%) were rated appropriately referred and 421 (58.7%) were considered hearing aid candidates. Overall, 71.5% of patients contacted felt their referral was appropriate. <h3>CONCLUSION</h3> An electronic alert used to remind clinicians to ask patients aged ≥55 years about hearing loss significantly increased audiology referrals for at-risk patients. Audiologic and audiogram data support the effectiveness of the prompt. Clinicians should consider adopting this method to identify patients with hearing loss to reduce its known and adverse sequelae.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".