Reasons for not seeking early dental care in patients presenting in the exodontia department at a Tertiary Care Hospital in Lahore, Pakistan.
Bibliographic record
Abstract
Objective: The aim was to gain insight on patient factors and administrative shortcomings in the dental health department that prevent patients from seeking timely dental care. Study Design: Cross Sectional Survey. Setting: Punjab Dental Hospital, Lahore, Pakistan. Period: 7 January 2020 to 28 February 2020. Material & Methods: A structured close ended questionnaire was filled by information obtained from 400 patients that presented in the Exodontia OPD requiring extractions due to irreversible tooth damage. The data was collected from 244 (61%) females and 156 (39%) males. It was analyzed using SPSS version 20.0 and post stratification chi-square test was applied to see the significance of all comparative statistics. P value of <0.05 was taken as significant. Results: Out of 400 patients, 350 had not visited a dentist when the symptoms first appeared. Of these n=213 (60.86%) were females and n=137 (39.14%) were males. Majority of the patients were in the age range of 40-65 years and belonged to the low socioeconomic class n=213 (60.9%). The main causes of patients presenting now were pain and swelling. The major reasons reported for delayed visit were: “Self-medication” n=113 (24.57%),” Symptoms will resolve on their own” n=79 (17.17%),” No awareness” n=64 (13.91%),” Non serious and lazy attitude” n=39 (8.48%) and “Busy schedule” n=36 (7.83%). Conclusion: Our findings suggested that only a small proportion of people visited the dentist when the symptoms first appeared. Most of the patients ignored their symptoms to such an extent that the only possible treatment that could be offered was extraction of the concerned tooth. There was a general misconception that dental diseases are not very serious and can be resolved by over the counter medications. Thus, more dental awareness programs should be incorporated in the government health policy to increase focus on oral health.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".