Exploration of a Multi-Parameter Technology for Pain Assessment in Postoperative Patients After Cardiac Surgery in the Intensive Care Unit: The Nociception Level Index (NOL)TM
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to explore the use of a multi-parameter technology, the Nociception Level (NOL) index (Medasense Biometrics Ltd, Ramat Gan, Israel), for pain assessment in postoperative awake patients after cardiac surgery during non-nociceptive and nociceptive procedures in the intensive care unit (ICU). MATERIALS AND METHODS: A prospective cohort repeated-measures design was used. Patients were included if they were in the ICU after undergoing cardiac surgery and if they could self-report their pain. A non-invasive probe was placed on the patient's finger for the continuous monitoring of the NOL index. Patients' self-reports of pain and anxiety (0-10 Numeric Rating Scale or NRS), and behavioral scores with the Critical-Care Pain Observation Tool (CPOT) were obtained before and during a non-nociceptive procedure (ie, non-invasive blood pressure [NIBP] using cuff inflation), and before, during and after a nociceptive procedure (ie, chest tube removal [CTR]) for a total of five time points. Non-parametric tests were used to compare scores at different time points, and receiver operating characteristic curve analysis was performed. RESULTS: Fifty-four patients were included in the analysis. The NOL index, pain and anxiety scores were significantly higher during CTR compared to rest and NIBP (p < 0.001). During CTR, the NOL was associated with self-reported pain intensity and unpleasantness but not with anxiety and CPOT scores. The NOL showed a modest performance in detecting pain (NRS ≥1 and ≥5) in this sample with sensitivity and specificity ranging from 61% to 85%. CONCLUSION: The NOL index was able to discriminate between a non-nociceptive and a nociceptive procedure and was associated with self-reported pain. Further validation testing of the NOL is necessary in a heterogeneous sample of ICU patients.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".