Bibliographic record
Abstract
In Reply: The focus of the current paper was the identification of vestibular impairment, specifically total bilateral vestibular loss (TBVL), through the use of a screening tool, in children with hearing loss, regardless of etiology. TBVL in children with hearing loss can occur for a number of reasons, one of which includes the risk related to cochlear implantation itself as the Letter Writer points out. Indeed, there is an extensive literature that suggests the causality of vestibular impairments as a result of cochlear implantation, the overwhelming majority of which result in partial or unilateral impairment. Such impairments are also seen in our implant population (manuscript in preparation). We agree with the Letter Writer's point that TBVL is theoretically possible following bilateral implantation. The 2% risk of TBVL following bilateral simultaneous implantation is an estimate based on the careful pre and postoperative study of vestibular function in children undergoing delayed sequential implantation and is accentuated by a single case of TBVL subsequent to, and suspected to be due to, implantation from that same group (15, 16). While this 2% should not be discounted, it is dwarfed by the risk of TBVL resulting from the underlying etiology of the deafness itself (i.e., meningitis, cochlear vestibular anomalies, cytomegalovirus, Usher Syndrome, etc.). As a result, the goal of the current paper was to increase the awareness and detection of combined cochleovestibular impairments in children presenting with hearing loss, a goal which we think was met by the publication in its original form. It's use as a platform to advocate for or against the simultaneous or sequential application of cochlear implants in this population distracts from its purpose of providing clinicians with the simple tools they require to recognize TBVL in their clinics.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.030 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".