Is It Painful or Not?
Bibliographic record
Abstract
OBJECTIVES: To evaluate the ability of the Behavioral Indicators of Infant Pain (BIIP) scale to discriminate between skin-breaking and nonskin breaking procedures, and to identify sensitized pain responses in preterm infants in the neonatal intensive care unit (NICU). METHODS: Sixty-nine infants born between 24 and 32 weeks gestational age were assessed at 32 weeks postconceptional age during blood collection on one day (procedure A), and then on another day during blood collection preceded by a diaper change (procedure B). Procedure order was randomized. Outcome measures were changes in BIIP coded from continuous bedside video recordings and changes in heart rate (HR). RESULTS: During blood collection (procedure A), BIIP scores (P<0.0001) and mean HR (P<0.0001) were higher than during the diaper change and higher when the infants had had a preceding diaper change (procedure B vs. procedure A) (P<0.03). HR changed from baseline to the stressors for each procedure. No differences in mean HR were observed during Lance phase between the procedure A and the B blood collection; however, HR remained elevated significantly during the Recovery phase when blood collection was preceded by the diaper change (P<0.03). DISCUSSION: The BIIP scale is reliable, accurate, and valid assessment for measuring acute pain in preterm infants in the NICU. This assessment combines the relatively most specific, anatomically based, theoretically derived indicators; and it allows evaluation of behavioral and physiologic pain responses separately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".