A CRITICAL REVIEW OF PAIN ASSESSMENT AND MANAGEMENT IN EXTREMELY LOW GESTATIONAL AGE INFANTS
Bibliographic record
Abstract
Objective The past 2 decades have witnessed an increased survival of extremely low gestational age (ELGA; 23–27 weeks GA) infants who are exposed to multiple painful procedures at a time of rapid neurological development. Pain in the developing nervous system differs from the mature nervous system, but little is known about how ELGA infants manifest pain. The aim is to systematically review the evidence of pain in ELGA infants to ascertain best pain practices. Methods A comprehensive electronic search was conducted in MEDLINE, CINAHL and EMBASE. Two individuals screened and extracted data independently from relevant papers. Results Only 3/13 papers focused on pain behaviors in ELGA neonates. In 2 studies, ELGA infants were examined during painful and non-painful situations. Increased facial actions and decreased body movement were indicators of pain, but magnitude of response was proportional to gestational age (GA). One study contributed to the construct validity of the Premature Infant Pain Profile, while a second contributed to the validity of a non-English version with more mature infants. Seven studies examined ELGA infants at 32 weeks GA; however pain stimuli (e.g. heel lance, clustered care) and outcome measures (e.g. facial, body movement) varied. Only one study examined the maturational effects on pain responses in 11 neonates. Conclusions Few studies have examined pain behaviors in ELGA, and most were derived from small sample sizes with single observations during heel lance procedures. Further longitudinal studies with adequate sample sizes examining a broad repertoire of responses for a variety of pain paradigms is required.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".