USING PRACTICE GUIDELINES TO ALLOCATE MEDICAL TECHNOLOGIES
Bibliographic record
Abstract
Clinical practice guidelines are expanding their scope of authority from clinical decision making to collective policy making, and promise to gain ground as resource allocation tools in coming years. A close examination of how guidelines approach patient selection criteria offers insight into their ethical implications when used as resource allocation or rationing instruments. The purposes of this paper are: a) to examine the structure of allocative reasoning found in clinical guidelines; b) to identify the ethical principles implied and compare how guidelines enact these principles with how explicit systems-level rationing exercises and health policy analyses have approached them; and c) to offer some preliminary suggestions for how these ethical issues might be addressed in the process of guideline development. The resulting framework can be used by guideline developers and users to understand and address some of the ethical issues raised by guidelines for the use of scarce technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.118 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".