Telemedicine in Tinnitus: Feasibility, Advantages, Limitations, and Perspectives
Bibliographic record
Abstract
Given the important patient needs for support and treatment, telemedicine-defined by medical approaches supported by the new technologies of information-could provide interesting alternative in tinnitus treatment. By analyzing the published tools and approaches which could be used in the context of telemedicine for tinnitus by health professionals or self-administrated by patients, this review summarizes, presents, and describes the principal telemedicine approaches available presently or in the near future to help assess or treat tinnitus or to offer support to tinnitus sufferers. Several pieces of evidence strongly support the feasibility of telemedicine approaches for tinnitus. Telemedicine can be used to help tinnitus sufferers at several points in the therapeutic process: for early screening, initial evaluation, and diagnosis; for optimizing therapeutic tools, particularly behavioural therapies and virtual reality-enhanced behavioral therapies; for long-term monitoring of patients and provision of online support. Several limitations are, however, discussed in order to optimize the safe development of such approaches. Cost effective and easy to implement, telemedicine is likely to represent an important part of the future of tinnitus therapies and should be progressively integrated by otolaryngologists.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".