A longitudinal study of the association between tooth loss and age‐related hearing loss
Bibliographic record
Abstract
The purpose of this study was to investigate cross-sectional and longitudinal associations between hearing acuity and tooth loss in 1156 US veterans taking part in the Veterans Affairs' Normative Aging (NAS) and Dental Longitudinal (DLS) Studies in the Boston, MA, area. The mean age was 48 years (SD = 8.9), 5.3% were edentulous, and 15.4% had < 17 teeth at baseline. Hearing acuity was determined by puretone, air- and bone-conduction audiometry, and speech discrimination tests at triennial examinations over a 20-year follow-up period. Hearing decline was defined as a change from baseline in the average puretone air-conduction thresholds of > or = 20 dB at 0.25, 0.5, 1, 2, 3, 4, 6, and 8 kHz. The explanatory variables of interest were change since baseline in dentate status (cut points at < 1, < 17, and < 20 teeth), and in the number of teeth lost (linear). Linear and logistic regression models--which controlled for baseline audiological status, age, air-bone gap, and otoscopic examination at current visit--showed that subjects who went from having > or = 17 to < 17 teeth had 1.64 times (95% CI, 1.24-2.17) as high odds of having hearing decline as those with no change in their dentate status. For every tooth lost since baseline, there was a 1.04 times as high odds (95% CI, 1.02-1.06) for hearing decline, when additional baseline and time-varying covariates were taken into account in the model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".