“Influence of socioeconomic status on caries score among primary school children of Peshawar”.
Bibliographic record
Abstract
Objectives: Dental caries is among common oral conditions in children and adults. Several studies and preventive measures have been carried out over the world to reduce dental caries rate. The objective of this study was to evaluate the relationship between caries score and socioeconomic status among children. Study Design: Cross-sectional study. Setting: Primary schools of Hayatabad, Peshawar. Period: months (January to June 2019). Material and Methods: 240 children aged from 3 to 5 years old were recruited in our study from government and private schools of Hayatabad, Peshawar. Socioeconomic status of the children’s parents was deduced by visiting government schools having lower fees and private schools having higher fees in Hayatabad, Peshawar. The frequency of dental caries among children was determined by clinical examination followed by decayed, extracted, filled teeth index. Results: In this study, 120 participants from private schools belonged to the upper class while the other 120 subjects from government schools belonged to the lower class. The mean DEFT value was found to be 30% greater in children of private schools. Conclusion: The study determined that the frequency of DEFT was found more in upper economic status as compared to the lower economic status, which shows association of socio-economic status with oral health condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".