Ethical considerations in resource allocation in a cochlear implant program.
Bibliographic record
Abstract
OBJECTIVE: To review processes of resource allocation and the ethical considerations relevant to the fair allocation of a limited number of cochlear implants to increasing numbers of potential recipients. DESIGN: Review of relevant considerations. SETTING: Tertiary referral hospital. METHODS: Editorial discussion of the ethical issues of resource allocation. MAIN OUTCOME MEASURES: Heterogeneity of audiometric thresholds, self-reported disability of hearing loss, age of the potential cochlear implant recipient, cost-effectiveness, access to resources, compliance with follow-up, social support available to the recipient, social consequences of hearing impairment, and other recipient-related factors. RESULTS: In a publicly funded health care system, there will always be a need for decision-making processes for allocation of finite fiscal resources. All candidates for cochlear implantation deserve fair consideration. However, they are a heterogeneous group in terms of needs and expected outcomes consisting of traditional and marginal candidates, with a wide range of benefit from acoustic amplification. CONCLUSIONS: We argue that implant programs should thoughtfully prioritize treatment on the basis of need and potential benefit. We reject queuing on the basis of "first-come, first-served" or on the basis of perceived social worth.
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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 | 0.273 | 0.539 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.023 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".