Quantification of neonatal procedural pain severity: a platform for estimating total pain burden in individual infants
Bibliographic record
Abstract
There is increasing evidence that long-term outcomes for infants born prematurely are adversely affected by repeated exposure to noxious procedures. These interventions vary widely, for example, in the extent of damage caused and duration. Neonatal intensive care unit (NICU) procedures are therefore likely to each contribute differently to the overall pain burden of individual neonates, ultimately having a different impact on their development. For researchers to quantify the procedural pain burden experienced by infants on NICU, we aimed to estimate the pain severity of common NICU procedures using published pain scores. We extracted pain scores over the first minute (pain reactivity) from the literature, using 59 randomized controlled trials for 15 different procedures. Hierarchical cluster analysis of average pain scores resulted in 5 discrete severity groups; mild (n = 1), mild to moderate (n = 3), moderate (n = 7), severe (n = 3), and very severe (n = 1). The estimate of the severity of individual procedures provided new insight into infant pain reactivity which is not always directly related to the invasiveness and duration of a procedure; thus, both heel lance and skin tape removal are moderately painful procedures. This estimate of procedural pain severity, based on pain reactivity scores, provides a novel platform for retrospective quantification of an individual neonate's pain burden due to NICU procedures. The addition of measures that reflect the recovery from each procedure, such as brain activity and behavioural regulation, would further improve estimates of the pain burden of neonatal intensive care.
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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 | 0.044 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".