Clinical indicators of nursing outcomes classification for patient with risk for perioperative positioning injury: A cohort study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To test the validity and reliability of Nursing Outcomes Classification outcomes and their clinical indicators for patients with the nursing diagnosis 'Risk for perioperative positioning injury'. BACKGROUND: Surgical positioning is an essential part of perioperative nursing practice. The use of a standardised language values the clinical evaluation of the perioperative nurse, reinforcing its contribution to surgical patient care. DESIGN: Longitudinal concept validation cohort study. METHODS: Patients were selected based on the operating room surgical schedule. The sample included adult patients who underwent elective surgical procedures requiring anaesthesia, classified as surgical class 2, 3 or 4. Outcomes were measured with an instrument, which included 33 clinical indicators for eight outcomes. The patients were assessed at five distinct time points in the perioperative phases. This study followed the STROBE guidelines. RESULTS: A total of 50 patients were included. Each underwent five clinical assessments, for a total of 250 documented assessments. Differences in evaluations were mostly related to reduced scores of clinical indicators in the immediate postsurgical time points, which recovered to the highest score at the end of the fifth (and last) evaluation. The results of factor analysis and Cronbach's alpha calculations suggested a new configuration for this nursing outcomes, consisting of five outcomes-Circulation Status, Tissue Perfusion: peripheral, Neurological Status: peripheral, Tissue Integrity: skin and mucous membranes and Thermoregulation-and 13 clinical indicators. CONCLUSIONS: Nursing Outcomes Classification outcomes and clinical indicators for the nursing diagnosis at 'Risk for perioperative positioning injury' are sensitive to patient states during the perioperative period. RELEVANCE TO CLINICAL PRACTICE: Use of nursing taxonomies during the perioperative period may contribute to the discussion on the role of perioperative nurses and their relevance in patient care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".