Examining Delayed Recall in Cochlear Implant Users Using the Montreal Cognitive Assessment, California Verbal Learning Test, Third Edition, and Item Specific Deficit Approach: Preliminary Results
Bibliographic record
Abstract
Purpose:Recent studies using the Montreal Cognitive Assessment (MoCA) suggest delayed recall is challenging for cochlear implant (CI) users. To better understand the underlying processes associated with delayed recall in CI users, we administered the MoCA and the California Verbal Learning Test, Third Edition (CVLT-3), which provides a more comprehensive assessment of delayed recall ability. Methods:The MoCA and CVLT-3 were administered to 18 high-performing CI users. For the CVLT-3, both the traditional scoring and a newer scoring method, the Item-Specific Deficit Approach (ISDA), were employed. Results:The original MoCA score and MoCA delayed recall subtest score did not relate to performance on any CVLT-3 measures regardless of scoring metric applied (i.e., traditional or ISDA). Encoding performance for both the CVLT-3 and ISDA were related. Consolidation, which is only distinctly defined by the ISDA, related to CVLT-3 cued delay recall performance but not free delay recall performance. Lastly, ISDA retrieval only related to CVLT-3 measures when modified. Conclusion:Performance on the MoCA and CVLT-3 in a high performing CI patient population were not related. We demonstrate that the ISDA can be successfully applied to CI users for the quantification and characterization of delayed recall ability; however, future work addressing lower performing CI users, and comparing to normal hearing controls is needed to determine the extent of potential translational applications. Our work also indicates that a modified ISDA retrieval score may be beneficial for evaluating CI users although additional work addressing the clinical relevance of this is still needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".