A step-by-step approach to patients leaving against medical advice (AMA) in the emergency department
Bibliographic record
Abstract
OBJECTIVES: Patients leaving against medical advice (AMA) can be distressing for emergency physicians trying to navigate the medical, social, psychological, and legal ramifications of the situation in a fast-paced and chaotic environment. To guide physicians in fulfilling their obligation of care, we aimed to synthesize the best approaches to patients leaving AMA. METHODS: We conducted a scoping review across various fields of work, research context and methodology to synthesize the most relevant strategies for emergency physicians attending patients leaving AMA. We searched Medline, CINAHL, PSYCHO Legal Source, PsycINFO, PsycEXTRA, Psychological and Behavioural Sciences collection, SocIndex and Scopus. Search strategies included controlled vocabulary (i.e., MESH) and keywords relevant to the subject chosen by a team of four people, including two specialized librarians. RESULTS: The literature review included 34 relevant papers about approaches to patients leaving AMA: 8 case presentations, 4 ethical case analyses, 10 legal letters, 4 reviews and 8 original studies. The main identified strategies were prioritizing a patient-centered approach, proposing alternative discharge and reducing harm while properly documenting the encounter. CONCLUSION: A systematic approach to patients leaving AMA could help improve patient care, support physicians and decrease stigmatization of this population. We advocate that emergency physicians should receive training on how to approach patients leaving AMA to limit the impact on this vulnerable population.
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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 | 0.035 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| 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".