Performance on the standard and hearing‐impaired Montreal Cognitive Assessment in cochlear implant users
Bibliographic record
Abstract
OBJECTIVES: Commonly used cognitive screening tools were not originally developed for patients with hearing loss (HL) and rely heavily on the ability to hear the instructions and test stimuli. Recently, the Montreal Cognitive Assessment (MoCA) was modified for use with hearing-impaired populations (ie, HI-MoCA). In order to investigate the clinical utility of the HI-MoCA, we assessed performance between the standard MoCA and HI-MoCA among postlingually deafened cochlear implant (CI) users. METHODS: We administered the standard MoCA and HI-MoCA to 21 CI users and compared their performance. We assessed differences in pass/fail status when items from the attention and language sections and the delayed recall task were removed. RESULTS: There was no significant difference in performance between the standard MoCA and HI-MoCA. Participants scored higher on both test versions when the delayed recall task was removed. Participants also performed better on the delayed recall task on the HI-MoCA than on the standard MoCA. CONCLUSIONS: While our findings suggest that the modality of presentation for the MoCA does not influence overall performance for postlingually deafened CI users, visual presentation of stimuli impacted performance on delayed recall. Furthermore, irrespective of presentation modality, our participants scored higher on both MoCA versions when the delayed recall task was removed. Clinically, modifications to the presentation of the MoCA might not be necessary for CI users; however, clinicians should be aware that the delayed recall task is inherently harder for these patients.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".