Sclerotherapy in oral cavity hemangioma with glucose and ethanolamine oleate. Case reports
Bibliographic record
Abstract
Introduction: the hemangioma is a vascular lesion of uncertain etiology. It is most fre- quently observed in the head and neck region (50% of the cases). In the oral cavity, com- monly affect the lower lip, tongue, jugal mucosa and palate. There are several therapeutic modalities to treat hemangiomas, among them; sclerotherapy stands out, since it represents a simple, comfortable, effective and low-cost method of treatment. Objective: compare the treatment between two sclerosing agents: the ethanolamine oleate 5% and glucose solution 50%. Side effects and period of treatment was also analyzed and illustrated with three clinical cases. Material and methods: three cases presenting diascopy blanching lesions, and vascular injury, were aspirated being collected bloody content, confirming the diagnosis of vascular injury, and establishing a clinical diagnosis of hemangioma. Infiltration anesthesia was applied at a distance, and slow implementation of sclerosing agents was injected, intralesionally. An interval of seven days between each application was maintained. Patients were asked about possible occurrence of side effects: burning, pain, swelling the region. Results: both sclerosing agents used in this study are efficient, easy to perform, with market availability and reduced cost. Conclusion: regarding the duration of the treatment, no significant difference between the sclerosing agents was observed, because both needed a few applications for the complete resolution of the lesions, no recurrence was observed and early side effects were pain, swelling, redness and burning.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".