Breast Metastasis around Dental Implants: A Case Report
Bibliographic record
Abstract
BACKGROUND: Metastases to the oral cavity and to the jaws are rare; hence, the clinical manifestations of the oral metastasis lesion could frequently be simulating general pathologic entities, making the diagnosis a challenging process to the dental team. Local factors, such as trauma, have been observed to facilitate the growth of blood-borne tumors. To this end, surgical procedures such as fixture placement might cause cancer cells to spread. PURPOSE: Careful clinical examination is a valuable help in diagnosing oral lesions, which can improve the quality of life of patients and reduce the risks of oral complications. MATERIALS AND METHODS: A female patient was referred to the clinic with symptoms of irritation, swelling, and pain associated with implants in the mandible and the maxilla. RESULTS: Clinical examination, x-ray, and histopathology revealed that the patients suffered from a metastatic lesion, primary tumor being an adenocarcinoma of the breast diagnosed at the same time. CONCLUSION: Optimal clinical examination in conjunction with radiography and histopathology is a necessity in order to discover malignant lesions in time. Routine dental check-ups must comprise more thorough soft-tissue examination.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".