Pattern of Partial Edentulism According to Kennedy’s Classification among Patients Presenting in Tertiary Care Hospital of Taxilla
Bibliographic record
Abstract
Aim & Objective: This study was conducted with the goal to figure out the pattern of partially dentate arches in patients reporting to Dental Hospital of Taxilla. Methodology: The study is descriptive cross sectional that was focused on patients visiting the outpatient department of HITEC-IMS Dental College, Taxilla. A total of 224 patients, compromising both males and females, were randomly selected using convenience sampling. Age of the patients was 15-70 years. Informed consent was taken and patients were then visually examined and missing teeth were noted along with demographic information. Partially dentate arches were then classified on the basis of Kennedy’s classification. Collected data was evaluated by using SPSS software (27.0). Chi- square test was applied for analysis. Results: 224 patients were studied for the pattern of partial edentulism.44.6% patients were presented with partial edentulism in maxilla and 55.3% in mandible. Kennedy class I was 12.9%, Kennedy class II was 25.9%, Kennedy Class III was 54% and Kennedy class IV was 7.2% reported among presenting patients. 45.5% males and 54.5% females show pattern of partial edentulism with Kennedy Class III as the most prevalent class in both maxilla and mandible. Conclusion: Among the partially dentate arches Kennedy class III is more frequent in mandible than in maxilla. No notable association between pattern of partial edentulism and gender is established. Keywords: Kennedy’s Classification, Partial edentulism, Removable Partial Denture(RPD)
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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 | 0.001 |
| 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) | 0.003 | 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".