Neonatal Facial Coding System for Assessing Postoperative Pain in Infants: Item Reduction is Valid and Feasible
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to: (1). evaluate the validity of the Neonatal Facial Coding System (NFCS) for assessment of postoperative pain and (2). explore whether the number of NFCS facial actions could be reduced for assessing postoperative pain. DESIGN: Prospective, observational study. PATIENTS: Thirty-seven children (0-18 months old) undergoing major abdominal or thoracic surgery. OUTCOME MEASURES: The outcome measures were the NFCS, COMFORT "behavior" scale, and a Visual Analog Scale (VAS), as well as heart rate, blood pressure, and catecholamine and morphine plasma concentrations. At 3-hour intervals during the first 24 hours after surgery, nurses recorded the children's heart rates and blood pressures and assigned COMFORT "behavior" and VAS scores. Simultaneously we videotaped the children's faces for NFCS coding. Plasma concentrations of catecholamine, morphine, and its metabolite M6G were determined just after surgery, and at 6, 12, and 24 hours postoperatively. RESULTS: All 10 NFCS items were combined into a single index of pain. This index was significantly associated with COMFORT "behavior" and VAS scores, and with heart rate and blood pressure, but not with catecholamine, morphine, or M6G plasma concentrations. Multidimensional scaling revealed that brow bulge, eye squeeze, nasolabial furrow, horizontal mouth stretch, and taut tongue could be combined into a reduced measure of pain. The remaining items were not interrelated. This reduced NFCS measure was also significantly associated with COMFORT "behavior" and VAS scores, and with heart rate and blood pressure, but not with the catecholamine, morphine, or M6G plasma concentrations. CONCLUSION: This study demonstrates that the NFCS is a reliable, feasible, and valid tool for assessing postoperative pain. The reduction of the NFCS to 5 items increases the specificity for pain assessment without reducing the sensitivity and validity for detecting changes in pain.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".