Development of cognitive screening test for the severely hearing impaired: Hearing‐impaired <scp>M</scp>o<scp>CA</scp>
Bibliographic record
Abstract
OBJECTIVES: To develop a version of the Montreal Cognitive Assessment (MoCA) to be administered to the severely hearing impaired (HI-MoCA), and to assess its performance in two groups of cognitively intact adults over the age of 60. STUDY TYPE: Test development followed by prospective subject recruitment. METHODS: The MoCA was converted into a timed PowerPoint (Microsoft Corp., Redmond, WA) presentation, and verbal instructions were converted into visual instructions. Two groups of subjects over the age of 60 were recruited. All subjects passed screening questionnaires to eliminate those with undiagnosed mild cognitive impairment. The first group had normal hearing (group 1). The second group was severely hearing impaired (group 2). Group 1 received either the MoCA or HI-MoCA test (T1). Six months later (T2), subjects were administered the test (MoCA or HI-MoCA) they had not received previously to determine equivalency. Group 2 received the HI-MoCA at T1 and again at T2 to determine test-retest reliability. RESULTS: One hundred and three subjects were recruited into group 1, with a score of 26.66 (HI-MoCA) versus 27.14 (MoCA). This was significant (P < 0.05), but scoring uses whole numerals and the 0.48 difference was found not clinically significant using post hoc sensitivity analyses. Forty-nine subjects were recruited into group 2. They scored 26.18 and 26.49 (HI-MoCA at T1 and T2). No significance was noted (P > 0.05), with a test-retest coefficient of 0.66. CONCLUSION: The HI-MoCA is easy to administer and reliable for screening cognitive impairment in the severely hearing impaired. No conversion factor is required in our prospectively tested cohort of cognitively intact subjects. LEVEL OF EVIDENCE: 1b. Laryngoscope, 127:S4-S11, 2017.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".