Bibliographic record
Abstract
Dr. B is on the seventh day of his rotation as medical director of the intensive care unit (ICU) when he receives a referral call about a patient in emergency who needs ICU admission for ventilation support. Dr. B examines his ICU census and notes that not only are there no ICU beds available but there is also a request from a thoracic surgeon for an ICU bed for a patient currently in the observation room, and there is a request from a nearby hospital to transfer one of their patients to Dr. B's ICU. Dr. C, a pediatrician, has been asked to chair her hospital drug formulary committee to examine new drugs and determine which ones should be provided from the hospital budget. She is aware that these decisions are complex and often controversial and is unsure how to proceed. What is priority setting? Priority setting involves deciding which resources to allocate to competing needs. It is a key component of every health system because, whether wealthy or poor, no system can afford to provide every service that it may wish to provide. Both publicly and privately funded systems have the challenge of delivering quality care within the limits of government budgets or enrollee and employer contributions. Within health systems, priority setting occurs at each decision level: micro (at the bedside or in clinical programs), meso (in hospitals or regional institutions), and macro (at the system-wide level).
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.031 |
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".