Bilateral sequential adult cochlear implantation: Who should receive priority in the context of a constrained health care system?
Bibliographic record
Abstract
Resource allocation decisions have become increasingly necessary as the cost of health care habitually increases. Bilateral (second side) adult cochlear implantation (CI) is an example of a novel technology with accruing evidence of benefit, yet expense has limited universal employ. Currently at our centers, bilateral implantation is only provided under research protocol. In this article, we discuss the need for a principled approach concerning the distribution of a second device, both during this period of investigation and if ultimately an insured service. Allocation strategies, while extensively addressed in some arenas, have yet to be developed for second-side sequential adult CI. We advocate that physicians must assume an explicit role when both caring for individual patients as well as administering health care programs. We review social justice theories that inform resource allocation macrodecisions, and include a defence of age-based considerations. Our approach to patient selection for adult second-side CI sequentially considers clinical criteria (directly addressed in the article), a willingness to participate in rigorous research, and a 65 year cut-off. Ultimately, we employ random blinded selection for allocating bilateral CI among the remaining similarly situated individuals. This approach functions impartially and in a manner that is transparent for both patient and physician.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".