Nurses’ perceived feasibility and the clinical utility of the Nociception Level (NOL™) Index for pain assessment in critically ill adults
Bibliographic record
Abstract
Introduction: The Nociception Level (NOL™) Index monitors nociception and related pain using multiple physiologic parameters through a non-invasive finger probe and is currently undergoing validation for pain assessment in the Intensive Care Unit (ICU). This study aimed to describe its feasibility and clinical utility from nurses’ perspectives, which is crucial for its potential adoption in clinical practice. Methods: This descriptive study involved ICU nurses who received a brief training (5-minute video and handout) as part of the validation process of the NOL Index in a medical-surgical ICU. Trained nurses who used the NOL Index at the ICU bedside completed a self-administered questionnaire on its feasibility and clinical utility on a 4-point scale. Scores of 3 and 4 supported its feasibility and clinical utility. Results: Of the 21 trained nurses, nine (43%) used the NOL on enrolled patients, but eight (38%) completed the questionnaire. Their average age was 34 years, and 63% were female. Feasibility indicators were endorsed by 95% of nurses supporting the NOL’s easy application, installation, and calibration, with clear usage instructions. Training strategies (i.e., video and handout) were rated positively with scores ≥ 3/4 by 95% of nurses. Nurses recommended adding more hands-on practice for the bedside use of the NOL. Clinical utility indicators were endorsed by 87.5 % of nurses suggesting it should remain an assistive, not a diagnostic tool. Indeed, several factors including patient’s hemodynamic instability and movements limit the NOL Index’s use in the ICU. Conclusion: Nurses perceived the NOL Index as a potential alter- native measure for pain assessment in mechanically ventilated and sedated ICU patients unable to self-report. Keywords: feasibility, clinical utility, nociception, pain assessment, intensive care unit
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".