Influence of professionally assessed normative orthodontic treatment need on adult OHRQoL
Bibliographic record
Abstract
Introduction: There is an increasing amount of research regarding the impact of malocclusions on OHRQoL. Reviews on this topic has mostly focused on children and adolescents, and therefore the aim of this thesis is to investigate the impact of professionally assessed normative orthodontic treatment need on adult OHRQOL.\nMaterials and methods: Four electronic databases (PubMed, EMBASE, Cochrane library and Google Scholar) were searched using a search syntax including orthodontic treatment need, malocclusion, OHRQoL. The following inclusion criteria were used: Adult study population, healthy study participants, no previous or ongoing orthodontic treatment, a focus on malocclusion and quality of life, malocclusions assessed by professionals, OHRQoL estimated using validated questionnaires, and full-text articles written in English or in the Scandinavian languages. Methodical quality was assessed according to the Newcastle-Ottawa scale.\nResults: A total of 1152 titles and abstracts were identified after duplicates were removed and 11 studies were included for the final analysis. The studies represented a large variation in sample size, methodology and geographical spread and were assessed as being of low to moderate methodological quality. All 11 studies presented significant associations between malocclusion (indices or specific malocclusions) and OHRQoL (domains or total scores). Regression analyses disclosed most of the associations as weak to moderate. \nConclusion: Adults with considerable malocclusions have significantly poorer OHRQoL than people with occlusions near or within norms. Future studies would benefit from longitudinal studies with large representative samples.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".