The Premature Infant Pain Profile-Revised (PIPP-R)
Bibliographic record
Abstract
OBJECTIVES: To describe revisions to the Premature Infant Pain Profile (PIPP) and initial construct validation and feasibility of the Premature Infant Pain Profile-Revised (PIPP-R). METHODS: The PIPP was revised to enhance validity and feasibility. To validate the PIPP-R, data from 2 randomized cross-over studies were utilized to: (1) calculate and compare PIPP and PIPP-R scores in extremely low gestational age infants undergoing a painful and nonpainful event (N=52; dataset #1) and (2) calculate and compare PIPP and PIPP-R scores in assessing the effectiveness of (a) sucrose, (b) non-nutritive sucking (NNS)+sucrose, and (c) facilitated tucking+NNS+sucrose during heel lance (N = 85; dataset #2). Pearson correlations between PIPP and PIPP-R scores were calculated, and Student t tests and 1-way analysis of variance were used to determine construct validity during painful and nonpainful events. To establish feasibility, a survey of 31 Neonatal Intensive Care Unit nurses was conducted. RESULTS: PIPP-R scores were significantly lower during nonpainful (mean, 8.3; SD = 2.9) compared with painful (mean, 9.9; SD=3.1; t95 = 4.51, P = 0.036) events in extremely low gestational age infants in dataset #1. In dataset #2, PIPP-R scores were significantly lower in infants 25 to 41 weeks gestation in the group receiving NNS+sucrose compared with the other 2 groups (F2,79 = 2.9, P<0.05). Overall, nurses rated the PIPP-R as feasible. DISCUSSION: Initial construct validation and feasibility of the PIPP-R was demonstrated. Further testing with infants of varying gestational ages, diagnoses, and pain conditions is required; as is exploration of PIPP-R in relation to other types of physiological and cognitive responses.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".