Variations in dermatoglyphic patterns in oral submucous fibrosis and leukoplakia patients with and without adverse oral habits
Bibliographic record
Abstract
Introduction: The present study was conducted to determine the comparative variations in dermatoglyphic patterns in patients without oral submucous fibrosis (OSMF) and leukoplakia and those having lesions, as well as to predict the occurrence of these diseases and initiate preventive measures in these high-risk patients. Materials and Methods: Dermatoglyphic patterns were collected from randomly selected 120 patients using 3M™ CSD200i. Single-digit Optical Scanner (3M™, Canada, 2015) with automatic capture mechanism was applied to capture finger prints of all the 10 fingers of patients, who were divided in control and test group with respective subgroups of leukoplakia and OSMF. Qualitative analysis of dermatoglyphic patterns in the different groups showed loops, arches, and whorls. Results: The collected data was subjected to analysis using Chi-square test for comparison between the groups; significant difference in P value was observed on comparison between dermatoglyphic patterns in patients with leukoplakia and those with adverse oral habits but without oral lesions (P = 0.00005), patients with OSMF and individuals with adverse oral habits but without oral lesions (P = 0.03), patients with OSMF and individuals without adverse oral habits and without oral lesions (P = 0.004), leukoplakia and OSMF (P = 0.007). Quantitative analysis including total finger ridge count was done by counting the number of ridges in all 10 fingers for all the patients in all the groups. Conclusion: The present study showed weak association in the loop pattern of patients with OSMF than leukoplakia, whorl pattern with adverse oral habits, without oral lesions, and arch pattern with OSMF. More controlled prospective trials are needed to affirm the association, if any, at larger homogeneous Indian sample in future to validate the finding.
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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 | 0.000 | 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) | 0.002 | 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".