A Comparative Study of Efficacy of Topical Phenytoin vs Conventional Wound Care in Diabetic Ulcers
Bibliographic record
Abstract
A common side effect of phenytoin (diphenylhydantoin) treatment for epilepsy is gingival hyperplasia. This stimulatory effect of phenytoin on connective tissue suggested possibility for its use in wound healing. Aims: The objective of this study was to assess the efficacy of topical phenytoin compared to conventional wound care in improving the healing process and to prove it as a relatively low cost and easy to use option in the management of diabetic ulcers. Methods and Material: In this quasi-experimental study, the data from 200 patients with diabetic ulcers was collected, 100 patients underwent topical phenytoin dressing, remaining 100 patients underwent conventional wound care. The results were compared after 14 days. The variables compared were: rate of granulation tissue formation as percentage of ulcer area covered, percentage of graft take up and duration of hospital stay. Statistical analysis used: The categorical variable was compared by chi-square test and continuous variables by Student t-test. A p value <0.05 was considered significant. Results: In study group , the mean rate of granulation tissue formation was 87.94% , mean graft take up was 92.31% and mean hospital stay was 32.26 days with negative culture sensitivity was 70%. The control group showed, the mean rate of granulation tissue formation was 74.64%, the mean graft take was 86.15% of total ulcer surface area and mean hospital stay was 54 days with negative culture sensitivity was 54% Conclusions: Topical phenytoin helps in faster healing with better graft take up and reduces hospital stay.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".