Reporting of medical errors: An intensive care unit experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To determine the occurrence and type of medical errors in an intensive care setting using a voluntary reporting method. DESIGN: Prospective, single-center, observational study. SETTING: The medical intensive care unit (19 beds) at an urban teaching hospital. PATIENTS: Adult patients requiring at least 48 hrs of intensive care. INTERVENTIONS: Prospective reporting of medical errors. MEASUREMENTS AND MAIN RESULTS: During a 6-month period, 232 medical events were reported involving 147 patients. A total of 2598 patient days were surveyed yielding 89.3 medical events reported per 1000 intensive care unit days. The source of the reports included nurses, who reported most of the medical events (59.1%), followed by physicians-in-training (27.2%) and intensive care unit attending physicians (2.6%). One hundred thirty (56.2%) medical events occurred within the intensive care unit and were judged to involve patient careproviders who were working directly in the intensive care unit area. One hundred and two (43.8%) medical events were commissions or omissions that occurred outside of the intensive care unit during patient transports or in the emergency department and hospital floors. Twenty-three (9.9%) medical events leading to a medical error resulted in the need for additional life-sustaining treatment, and seven (3.0%) medical errors may have contributed to patient deaths. CONCLUSION: Medical errors appear to be common among patients requiring intensive care. Medical events resulting in an error can result in the need for additional life-sustaining treatments and, in some circumstances, can contribute to patient death. Patient healthcare providers appear to be in a unique position to identify medical errors. Institutions should develop formalized methods for the reporting and analysis of medical errors to improve patient care.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it