Comparing remote programming of cochlear implants using two methods: portable laptop and remote hosted site
Bibliographic record
Abstract
Objectives Compare two remote programming methods as a clinical service for user satisfaction, ease of use, preparation time and accessibility.Method Method 1 (Portable Laptop): A ‘Programming Kit’ including laptop was shipped to cochlear implant users’ homes (N = 20). The audiologist at the implant center used remote desktop control of this laptop to adjust subjects’ speech processors. Method 2 (Remote Hosted Site): Eight distant clinics were recruited as host sites to house cochlear implant programming hardware and software so that CI users (N = 19) could attend their facility. The audiologist at the implant center used remote desktop control of the host sites’ computers to adjust the subjects’ CI speech processors. All parties were asked to fill out a questionnaire following their remote session.Results Remote hosted site method was rated higher for ease of use by the Remote Experts (12/15, 80%), compared to portable laptop method (11/19, 57.9%) and is more accessible to CI users of all levels of computer abilities while requiring less preparation time per session.Conclusion Remote hosted site method is an easier, more efficient method of remote programming as a clinical service delivery method compared to the Portable Laptop.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".