Introduction to the 14th International Symposium on Cochlear Implants and other Implantable Auditory Technologies, Toronto, Canada, May 11 to 14, 2016
Bibliographic record
Abstract
We are extremely proud of the success of the 14th International Conference on Cochlear Implants and other Implantable Auditory Technologies in Toronto (May 12–14, 2016). In collaboration with the American Cochlear Implant Alliance (http://www.acialliance.org/), the conference convened for the first time in Canada—64 countries were represented by 1,646 registrants that included physicians, audiologists, speech pathologists, scientists, educators, learners, industry participants, and others. The scientific program addressed all of the current issues related to this field of endeavor. Importantly, the overarching themes of auditory plasticity, rehabilitation, and aging were prominently highlighted by the five keynote speakers: Michael Dorman, Charles Limb, Steve Lomber, Sandra Black, and Warren Estabrooks. A vast array of podium and poster presentations, punctuated by panels, and a basic science symposium in regenerative biology appealed to a diverse audience. The responses were overwhelmingly positive. In addition, we elected to recognize four surgeons for their outstanding achievements and commitment to this field of endeavor. All were individually honored and presented with citations acknowledging their lifelong commitment to the advancement of this technology. Drs. William Gibson of Sydney, Australia, Thomas Lenarz of Hanover, Germany, Thomas Balkany of Miami, USA, and Henyrk Skarzynski of Warsaw, Poland were the recipients. For this issue of Otology and Neurotology, we identified a number of presentations that reflected the themes and the high caliber of the scientific program. We do think that this selection will provide the reader with insights from acknowledged world leading investigators. Clearly, a meeting of this magnitude was not possible without the help of a small army of volunteers. The reviewers of abstracts, keynote speakers, panel moderators, and participants as well as podium and poster presenters were integral to our success. We would like to take this opportunity to thank Otology and Neurotology, Lippincott Williams & Wilkins, Editor-in-Chief Larry Lustig, and Publisher Ginny Pittman for this opportunity. We are grateful for the support and encouragement of the American Cochlear Implant Alliance in organizing this to date largest cochlear implant meeting held in North America.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.259 | 0.081 |
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".