The Premature Infant Pain Profile: Evaluation 13 Years After Development
Bibliographic record
Abstract
OBJECTIVE: To review the (1) reliability, validation, feasibility, and clinical utility and (2) the use of the Premature Infant Pain Profile (PIPP) from 1996 to 2009 to determine the effectiveness of pain management strategies. METHODS: Data sources included MEDLINE, CINAHL, EMBASE, PsycINFO, and the Web of Science. Published studies evaluating the measurement properties of the PIPP and intervention studies using the PIPP as an outcome measure of acute pain were included. One reviewer screened studies for relevance and inclusion. Four reviewers rated intervention studies for methodological quality and extracted data for the evidence tables. RESULTS: Of the 62 studies included, 14 focused on the measurement properties of the PIPP. Reliability of the PIPP was supported in 5 studies and construct validation was supported in 13 studies. The feasibility of the PIPP was addressed in 4 studies, whereas clinical utility was discussed in 2 studies. Twenty-seven of the 48 studies that were considered to have high methodological quality used the PIPP as the major outcome to evaluate the effectiveness of pain management interventions in infants. DISCUSSION: The PIPP continues to be a reliable and valid measure of acute pain in infants with numerous positive validation studies. There is substantial support for the use of the PIPP as an effective outcome measure in pain intervention studies in infants. Further research with health professionals is required to better support the feasibility and clinical utility of this measure.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".