A Prospective Determination of the Incidence of Perceived Inappropriate Care in Critically Ill Patients
Bibliographic record
Abstract
BACKGROUND: Health care providers' perceptions regarding appropriateness in end-of-life treatments have been widely studied. While nurses and physicians believe that rationing and other cost-related practices sometimes occur in the intensive care unit (ICU), they allege that treatment is often excessive. OBJECTIVE: To prospectively determine the incidence and causes of health care providers' perceptions regarding appropriateness of end-of-life treatments. METHODS: The present prospective study collected data from patients admitted to the medical-surgical trauma ICU of a 30-bed, Canadian teaching hospital over a three-month period. Daily surveys were completed independently by bedside nurses, charge nurses and attending physician. RESULTS: In total, 5224 of 6558 expected surveys (representing 294 patients) were analyzed, yielding a response rate of 79.7%. The incidence of perceived inappropriate care in the present study was 6.5% (19 of 294 patients), with ongoing treatment for >2 days after this determination occurring in 1% (three of 294 patients). However, at least one caregiver perceived inappropriate care at some point in 110 of 294 (37.5%) patients. In these cases, in which processes to address care were not already underway, respondents believed that important issues resulting in provision of inappropriate treatments included patient-family issues and communication before or in the ICU. Caregivers did not know their patients' wishes 22% (1129 of 5224) of the time. CONCLUSIONS: Although ongoing inappropriate care appeared to be a rare occurrence, the issue was a concern to at least one caregiver in one-third of cases. Public awareness for end-of-life issues, adequate communication, and up-to-date knowledge and practice in determining the wishes of critically ill patients are potential target areas to improve end-of-life care and reduce inappropriate care in the ICU. A daily, prospective survey of multidisciplinary caregivers, such as the survey used in the present study, is a viable and valuable means of determining the scope and causes of inappropriate care in the ICU.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".