The Confusion Assessment Method—A Tool for Delirium Detection by the Acute Pain Service
Bibliographic record
Abstract
INTRODUCTION: Delirium is an acute fluctuating disturbance in cognitive status, linked to increased morbidity and mortality. The purpose of this pilot study was to assess the feasibility in terms of required time and yield of delirium monitoring by the Acute Pain Service (APS) using the Confusion Assessment Method for Intensive Care Unit instrument. METHODS: Patients undergoing surgery requiring more than 2 days of hospital stay were recruited. Each patient was assessed daily for 2 days after surgery using the Confusion Assessment Method for Intensive Care Unit. Patients were also assessed for orientation to person, place, and time. Any notes of confusion or delirium made by physicians or nursing staff were gathered. RESULTS: 145 patients were recruited. Each patient encounter required an average 2.3 +/- 0.3 minutes for the assessment (95% CI). The incidence of delirium within 2 days after surgery was 7.6%. Only 18% of the patients diagnosed with delirium by the APS were noted as being confused by the medical or nursing staff. CONCLUSIONS: The use of this tool required little training, and only 2 minutes per patient. It detected more patients with delirium than did the standard nursing assessments or other patient-clinician interactions. The use of this instrument by the pain service was feasible in terms of time consumption and most likely would be valuable in its yield. Early detection may help in initiating prompt treatment, eliminating known risk factors and thus reducing morbidity.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".