Accuracy and Quality in the Nursing Documentation of Pressure Ulcers
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy and describe the quality of nursing documentation of pressure ulcers in a hospital care setting. DESIGN: A cross-sectional survey was used comparing retrospective audits of nursing documentation of pressure ulcers to previous physical examinations of patients. SETTING AND SUBJECTS: All inpatient records (n = 413) from February 5, 2002, at the surgical/orthopedic (n = 144), medical (n = 182), and geriatric (n = 87) departments of one Swedish University hospital. INSTRUMENTS: The European Pressure Ulcer Advisory Panel data collection form and the Comprehensiveness In Nursing Documentation. METHODS: All 413 records were reviewed for presence of notes on pressure ulcers; the findings were compared with the previous examination of patients' skin condition. Records with notes on pressure ulcers (n = 59) were audited using the European Pressure Ulcer Advisory Panel and Comprehensiveness In Nursing Documentation instruments. RESULTS: The overall prevalence of pressure ulcers obtained by audit of patient records was 14.3% compared to 33.3% when the patients' skin was examined. The lack of accuracy was most evident in the documentation of grade 1 pressure ulcers. The quality of the nursing documentation of pressure ulcer (n = 59) was generally poor. CONCLUSIONS: Patient records did not present valid and reliable data about pressure ulcers. There is a need for guidelines to support the care planning process and facilitate the use of research-based knowledge in clinical practice. More attention must be focused on the quality of clinical data to make proper use of electronic patient records in the future.
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 | 0.032 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".