Priority Setting in Neurosurgery as Exemplified by an Everyday Challenge
Bibliographic record
Abstract
BACKGROUND: The allocation of limited healthcare resources poses a constant challenge for clinicians. One everyday example is the prioritization of elective neurosurgical operating room (OR) time in circumstances where cancellations may be encountered. The bioethical framework, Accountability for Reasonableness (A4R) may inform such decisions by establishing conditions that should be met for ethically-justifiable priority setting. OBJECTIVE: Here, we describe our experience in implementing A4R to guide decisions regarding elective OR prioritization. METHODS: The four primary expectations of the A4R process are: (1) relevance, namely achieved by support for the process and criteria for decisions amongst all stakeholders; (2) publicity, satisfied by the effective communication of the results of the deliberation; (3) challengeability through a fair appeals process; and (4) Oversight of the process to ensure that opportunities for its improvement are available. RESULTS: A4R may be applied to inform OR time prioritization, with benefits to patients, surgeons and the institution itself. We discuss various case-, patient-, and surgeon-related factors that may be incorporated into the decision-making process. Furthermore, we explore challenges encountered in the implementation of this process, including the need for timely neurosurgical decision-making and the presence of hospital-based power imbalances. CONCLUSION: The authors recommend the implementation of a fair, deliberative process to inform priority setting in neurosurgery, as demonstrated by the application of the A4R framework to allocate limited OR time.
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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 | 0.084 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.038 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".