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
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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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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 | 0.009 | 0.074 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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