Operationalizing Equity in Surgical Prioritization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The allocation of critical care resources and triaging patients garnered a great deal of attention during the COVID-19 pandemic, but there is a paucity of guidance regarding the ethical aspects of resource allocation and patient prioritization in ‘normal’ circumstances for Canadian healthcare systems. One context where allocation and prioritization decisions are required are surgical waitlists, which have been globally exacerbated due to the COVID-19 pandemic. In this paper, we detail the process used to develop an ethics framework to support prioritization for elective surgery at The Hospital for Sick Children, Toronto, a tertiary pediatric hospital. Our goal was to provide guidance for the more value-laden aspects of prioritization, particularly when clinical urgency alone is insufficient to dictate priority. With this goal in mind, we worked to capture familial, relational, and equity considerations. As part of our institution’s concerted efforts to ethically and effectively address our surgical backlog, an ethics working group was formed comprising clinicians from surgery, anesthesiology, intensive care, a hospital bioethicist, a parent advisor, and an academic bioethics researcher. A reflective equilibrium process was used to develop an ethics framework. To this end, the same methodology was used to create a support for patient prioritization that identifies clinically and morally relevant factors for prioritization among medically similar surgical cases, with a substantive goal being to identify and redress health inequities in surgical prioritization, inasmuch as this is possible. While further steps are needed to validate several aspects of the framework, our research suggests that an ethics framework grounded in the practical realities of hospital operations provides consistency, transparency, and needed support for decisions that are often left to individual clinicians, as well as an opportunity to reflect upon the presence of health inequities in all domains of healthcare delivery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it