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Is the ABC pain scale reliable for premature babies?

2007· article· en· W1550245687 on OpenAlexaff
CV Bellieni, Marianna Maffei, Gina Ancora, Duccio Maria Cordelli, Maura Mastrocola, Giacomo Faldella, Emanuela Ferretti, Giuseppe Buonocore

Bibliographic record

VenueActa Paediatrica · 2007
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsHôpital Fleurimont
Fundersnot available
KeywordsCryingMedicineKappaConcurrent validityPain scaleVisual analogue scaleReliability (semiconductor)CorrelationScale (ratio)PediatricsPhysical therapyPsychometricsPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.271
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2007
Admission routes1
Has abstractyes

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