Is the ABC pain scale reliable for premature babies?
Bibliographic record
Abstract
AIM: We recently developed the ABC scale to assess pain in term newborns. The aim of the present study was to assess the reliability of the scale in preterm babies. MATERIAL AND METHODS: The scale consists of three cry parameters: (a) pitch of the first cry, (b) rhythmicity of the bout of crying and (c) cry constancy. Changes in these parameters were previously found to distinguish medium and high levels of pain as evaluated by spectral analysis of crying. We enrolled 72 babies to perform the steps usually requested to validate a scale, namely the study of the concurrent validity, specificity and sensibility. Moreover, we assessed the interjudge reliability and the clinical utility and ease of the scale. RESULTS: A good correlation (r = 0.68; r(2)= 0.45; p < 0.0001) was found between scores obtained with the ABC scale and the premature infant pain profile (PIPP) scale, demonstrating a good concurrent validity. The scale also showed good sensitivity and specificity (we found statistically significant differences between mean values of scores obtained in babies who underwent pain and babies who underwent non-painful stimulus.) Interobserver reliability was good: Cohen's kappa = 0.7. CONCLUSION: The good correlation between the two scales shows that the ABC scale is also reliable for premature babies.
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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".