Musical Rehabilitation in Adult Cochlear Implant Recipients With a Self-administered Software
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to determine if a self-administered computer-based rehabilitation program could improve music appreciation and speech understanding in adults who have a cochlear implant (CI). STUDY DESIGN: Prospective study. SETTING: Tertiary adult CI program. PATIENTS: Twenty-one postlingually deafened cochlear implant users between the ages of 27 and 79 years were recruited. INTERVENTIONS(S): A self-administered music rehabilitative software was designed to help improve the perception of musical patterns of increasing complexity, as well as pitch and timbre perception, premised on focused and divided attention. All participants completed a diagnostic music test before and after rehabilitative training, including tests of pitch and timbre perception and pattern identification with increasing levels of difficulty. Speech data in quiet and noise was also collected both pre- and post-training. Participants trained for a minimum of 3.5 hours a week, for 4 weeks. MAIN OUTCOME MEASURE(S): Mean changes in music perception and enjoyment as well as speech perception (IEEE sentence test in quiet and noise). RESULTS: Post-training diagnostic test scores, as compared with pretraining scores, indicated significant improvements in musical pattern perception. Tests of speech perception in quiet and in noise were significantly improved in a subset of this cohort. All of the training participants thought that the training helped to improve their recognition skills, and found the program to be beneficial. CONCLUSION: Despite the limitations of current CI technology, the results of this study suggest that auditory training can improve music perception skills, and possibly speech intelligibility, lending further support to rehabilitation being an integral part of the postimplantation paradigm.
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 | 0.000 | 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) | 0.002 | 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".