Access to intensive care unit beds for neurosurgery patients: a qualitative case study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to describe the process used to decide which patients are admitted to the intensive care unit (ICU) at a hospital with special focus on access for neurosurgery patients, and evaluate it using "accountability for reasonableness". METHODS: Qualitative case study methodology was used. Data were collected from documents, interviews with key informants, and observations. The data were subjected to thematic analysis and evaluated using the four conditions of "accountability for reasonableness" (relevance, publicity, appeals, enforcement) to identify good practices and opportunities for improvement. RESULTS: ICU admissions were based on the referring physician's assessment of the medical need of the patient for an ICU bed. Non-medical criteria (for example, family wishes) also influenced admission decisions. Although there was an ICU bed allocation policy, patient need always superceded the bed allocation policy. ICU admission guidelines were not used. Admission decisions and reasons were disseminated to the ICU charge nurse, the bed coordinator, the ICU resident, the intensivist, and the requesting physician/surgeon by word of mouth and by written documentation in the patient's chart, but not to the patient or family. Appeals occurred informally, through negotiations between clinicians. Enforcement of relevance, publicity, and appeals was felt to be either non-existent or deficient. CONCLUSIONS: Conducting a case study of priority setting decisions for patients requiring ICU beds, with a special focus on neurosurgical patients, and applying the ethical framework "accountability for reasonableness" can help critical care units improve the fairness of their priority setting.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".