Children’s self-reported pain at the dentist
Bibliographic record
Abstract
The aim of the present study is to get an insight into the pain report of children over two sequential dental visits. Furthermore, it was studied whether age, previous dental experience, level of dental anxiety and injection site were of influence on the self-reported pain of children during the first and second treatment session. One hundred and forty-seven children (4-11 years old) were included in the study. After receiving a local anesthesia injection prior to their dental treatment, they were asked how much pain they had felt. The level of dental anxiety was measured once by the parental version of the Dental Subscale of the Children's Fear Survey Schedule. Young children with a low level of dental anxiety show a sensitized reaction trend for self-reported pain over two sequential dental visits. Young children with a high level of dental anxiety reported the most pain on the first treatment session. For the older children, the children having previous dental experience gave the highest pain ratings on the first treatment session. Furthermore, for both young and older children the amount of pain reported for the second injection was best predicted by the amount of pain reported for the first injection, whereby higher scores the first time predict higher scores the second time. In conclusion, the memory of previous experience with dentistry and earlier treatment sessions seems of great influence on the behaviour and the experience of children during subsequent treatment sessions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".