Anxiety and Pain Severity in Children Based on Self-Report
Bibliographic record
Abstract
Background and Objective: Evaluation of the severity of pain in children can help the medical team diagnose the type of disease. In this study, anxiety and pain intensity in children were examined based on self-report. Materials and Methods: This cross-sectional study was performed in 2018 on 300 children aged 3 to 12 years, referring to outpatient treatment centres in Kerman. To measure the severity of pain felt by children, FPS-R was used. The level declared by children was evaluated by the pain intensity estimated by parents and doctors using a visual analogue scale (VAS) and the standard FLACC (Face, Legs, Activity, Cry, Consolability scale) for correlation. The data were analysed using SPSS software version 25. Results: The pain reported by children was obtained by VAS (4.16 ± 3.49), and the estimated pain by the doctor was obtained by FPS-R (2.87±1.68). The pain severity estimated by the doctor using FLACC had the highest correlation with the pain estimated by the doctor using VAS and the lowest correlation with the pain estimated by the mother using VAS. Conclusion: The results of this study showed that FPS-R could be used as a suitable self-report tool in children and, along with the standard FLACC, can help the medical team recognize the severity of children's pain.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".