Crying out in pain—A systematic review into the validity of vocalization as an indicator for pain
Bibliographic record
Abstract
BACKGROUND: Vocalization is often used to assess pain, sometimes combined with other behaviours such as facial expressions. Contrary to facial expressions, however, for vocalization, there is little evidence available on the association with pain. The aim of this systematic review was to critically analyse the association between vocalization and pain, to explore if vocalizations can be used as a "stand-alone" indicator for pain. METHODS: The search was performed according to the Prisma Guidelines for systematic reviews and meta-analysis. The following terms were used: "Pain Measurement," "Vocalization" and "Verbalization." The study population included verbal and non-verbal individuals, including older people and children. The search was performed in three different databases: PubMed, Embase and CINAHL. A total of 35 studies were selected for detailed investigation. Quality assessments were made using two grading systems: Grading of Recommendations Assessment Development and Evaluation system and the Newcastle-Ottawa scale. RESULTS: An association between vocalization and pain was found in most studies, particularly when different types of vocalizations were included in the investigation. Different types of vocalization, but also different types of pain, shape this association. The association is observed within all groups of individuals, although age, amongst others, may have an influence on preferred type of vocalization. CONCLUSIONS: There is an association between vocalization and pain. However, vocalization as a "stand-alone" indicator for pain indicates only a limited aspect of this multifactorial phenomenon. Using vocalization as an indicator for pain may be more reliable if other pain indicators are also taken into account. SIGNIFICANCE: Vocalizations are frequently used in pain scales, although not yet thoroughly investigated as a "single indicator" for pain, like, e.g. facial expression. This review confirms the role of vocalizations in pain scales, and stresses that vocalizations might be more reliable if used in combination with other pain indicators.
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".