A life‐course approach to assess the relationship between social and psychological circumstances and gingival status in adolescents
Bibliographic record
Abstract
BACKGROUND: Several models have been proposed to explain the causes of periodontal diseases. None have adopted the life-course approach. OBJECTIVE: To investigate the relationship between social, psychosocial and biological conditions experienced in early life and through the life course and gingival bleeding on probing. METHODS: A two-phase study was carried out in Brazil. In Phase I, 652 13-year-olds were clinically examined and interviewed. In Phase II, 311 families were randomly selected for an interview to collect information on the teenager's state at birth and selected impacts in their early years of life. Clinical examination included assessment of dental caries, periodontal and dental trauma status. The data analysis used logistic regression and the models were determined using stepwise procedure. FINDINGS: Adolescents who were born in a non-brick house, who were living in an overcrowded house at 13 years of age, those whose mother had less than 8 years of education, who were at a lower school grade for their age, those who reported high levels of paternal punishment and those who were from reconstituted families were significantly more likely to experience high levels of bleeding gums after probing. CONCLUSION: Early life and life-course experiences were important determinants of the level of gingival bleeding after probing in adolescents.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".