O-DEM: ein neues kognitives Screening bei Schwerhörigkeit
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".