Rationing Rotational Magnet Cochlear Implant Technology in a Single Payer Healthcare System
Bibliographic record
Abstract
INTRODUCTION: In a publicly funded health care system, fiscally responsible management of any program is required. This is especially pertinent as evolving technology and associated incremental costs, places pressure on device availability within a fixed funding envelope. The application of rotational magnet technology and associated escalating surgical wait times must be justified to patients and the single-payer system. We present a single cochlear implant center's attempt at a rationing schema for magnetic resonance compatible cochlear implantation. Contrasting approaches to rationing care are evaluated and deliberated. METHODS: Based on a comparison of magnetic resonance imaging (MRI) rates within the general population to our cochlear implant (CI) cohort, we attempt the development of a decision-making schema that maximizes the number of patients to receive a CI while rationing the distribution of a rotational magnet technology to similarly situated individuals most likely to benefit. RESULTS: We elect to provide rotational magnet technology to select patient cohorts. This is based on the dominant imaging needs of these populations and the probability of requiring recurrent imaging studies. We consider this an ethical approach grounded in the egalitarian principle of equality of opportunity within cohorts of patients. CONCLUSION: Given finite resources, increasing per unit cost will unavoidably extend wait times for adult patients. Our approach does not afford similar implant devices for all patients, but rather all similarly situated individuals. Therefore, access to a scare medical resource requires program rigor and a formalized policy around candidacy for emergent technology.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".