Initial validation of the Behavioral Indicators of Infant Pain (BIIP)
Bibliographic record
Abstract
Accurate pain assessment in preterm infants in the neonatal intensive care unit (NICU) is complex. Infants who are born at early gestational ages (GA), and who have had greater early pain exposure, have dampened facial responses which may lead to under-treatment. Since behavioral and physiological responses to pain in infants are often dissociated, using multidimensional scales which combine these indicators into a single score may limit our ability to determine the effects of interventions on each system. Our aim was to design a unidimensional scale which would combine the relatively most specific, individual, behavioral indicators for assessing acute pain in this population. The Behavioral Indicators of Infant Pain (BIIP) combines sleep/wake states, 5 facial actions and 2 hand actions. Ninety-two infants born between 23 and 32 weeks GA were assessed during 3, 1 min Phases of blood collection. Outcome measures included changes in BIIP and in Neonatal Infant Pain Scale (NIPS) scores coded in real time from continuous bedside video recordings; changes in heart rate (HR) were obtained using custom physiological processing software. Scores on the BIIP changed significantly across Phases of blood collection (p<0.01). Internal consistency (0.82) and inter-rater reliability (0.80-0.92) were high. Correlations between the BIIP and NIPS were modest (r=0.64, p<0.01) as were correlations between the BIIP and mean heart rate (r=0.45, p<0.01). In this initial study, the BIIP has been shown to be a reliable, valid scale for assessing acute pain in preterm infants in the NICU.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".