Comparison of behind-the-ear vs. off-the-ear speech processors in cochlear implants: A systematic review and narrative synthesis
Bibliographic record
Abstract
BACKGROUND: Cochlear implants (CI) with off-the-ear (OTE) and behind-the-ear (BTE) speech processors differ in user experience and audiological performance, impacting speech perception, comfort, and satisfaction. OBJECTIVES: This systematic review explores audiological outcomes (speech perception in quiet and noise) and non-audiological factors (device handling, comfort, cosmetics, overall satisfaction) of OTE and BTE speech processors in CI recipients. METHODS: We conducted a systematic review following PRISMA-S guidelines, examining Medline, Embase, Cochrane Library, Scopus, and ProQuest Dissertations and Theses. Data encompassed recipient characteristics, processor usage, speech perception, and non-audiological factors. Studies were assessed for quality and risk of bias by using Newcastle-Ottawa Scale (NOS). RESULTS: Nine studies involving 204 CI recipients, with a mean age of 49.01 years and 6.62 years of processor use, were included. Audiological results indicated comparable performance in quiet environments, with a slight preference for OTE in noisy conditions. For non-audiological factors, OTE processors excelled in comfort, handling, and aesthetics, leading to higher satisfaction. More data on medical complications and long-term implications is needed. CONCLUSION: OTE processors may offer comparable performance to BTE processors in certain conditions, though not universally across all audiological outcomes. Interpretation depends on settings, processor generation, and testing paradigms. However, non-audiological factors might favour OTE. Understanding current literature may guide professionals in selecting suitable processors for CI recipients.
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".