O-DEM: ein neues kognitives Screening bei Schwerhörigkeit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Hearing loss is a significant risk factor for dementia. To date, cognitive impairment and dementia in patients with hearing impairment (HI) cannot be adequately diagnosed by commonly administered cognitive screening tests due to sensory impairments. Therefore, an adapted screening is needed. The aim of the present study was to develop and evaluate a cognitive screening for people with HI. MATERIALS AND METHODS: The new cognitive screening, called O‑DEM, entails a word fluency test, the Trail Making Test A (TMT-A), and a subtraction task. First, the O‑DEM was tested in a large clinical sample (N = 2837) of people without subjective HI. In a second step, the O‑DEM was evaluated in 213 patients with objectively assessed HI and compared with the Hearing-Impaired Montreal Cognitive Assessment (HI-MoCA). RESULTS: The results indicate that the O‑DEM subtests significantly discriminate between participants with no, mild, and moderate to severe cognitive impairment. Based on the mean and standard deviation of the participants without cognitive impairment, a transformation of the raw scores was performed and a total score with a maximum value of 10 was determined. In the second part of the study, the O‑DEM was shown to be as sensitive as the HI-MoCA in differentiating between people with and without cognitive impairment. CONCLUSION: Compared to other screenings, the O‑DEM is a quickly administrable screening for the detection of mild and moderate cognitive impairment in people with HI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
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 it