Reasons for seeking orthodontic treatment in Lahore population: A cross-sectional survey in a low-income country
Bibliographic record
Abstract
Introduction: Poor esthetics, dysfunction and discomfort are the key reasons for seeking orthodontic treatment across the world as reported by many researchers. This paper presents the causative factor for seeking orthodontic treatment in the patients who are visiting Punjab Dental Hospital of a populous city Lahore (de' Montmorency College of Dentistry) in local settings and associating these reasons with demographic characteristics. Objective: Aim of this cross-sectional survey was to explore the reasons for seeking orthodontic treatment among individuals who are visiting PDH. Materials and methods: This study was carried out in Punjab Dental Hospital (PDH) after the approval of the Institutional Review Board (IRB) on a sample of 98 individuals having malocclusion assessed with Angle's classification of the malocclusion. We chose simple random sampling. A self-structure questionnaire was designed to get data by the principal investigator after taking verbal and written consent. Descriptive statistics were calculated using SPSS 21. Chi-square test of association was applied to associate reasons with different demographic variables. P-value <0.05 was taken as significant. Results: Female respondents were more in number than males. Around one-third of respondents (30.6 %) had a monthly income of less than 25000 PKR ($ 170). Esthetics was the primary reason for seeking orthodontic treatment. The most common type of malocclusion was the Class II malocclusion. Statistically significant factors that emerged in this study that turned into reasons for seeking orthodontic treatment were hurdles in marriage, referral by a general dentist, motivation by parents, self-esteem and speech problems. Conclusion: In conclusion, patients seek orthodontic treatment mainly to enhance facial esthetics and self-confidence, motivation by the parents, and social acceptability.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".