Cronkhite-Canada syndrome: A case report and review of the literature
Bibliographic record
Abstract
Cronkhite -Canada Syndrome (CCS) is a rare non-hereditary disease characterized by multiple polyps in the alimentary tract and ectoderm changes, and there is no clearly diagnostic criteria and treatment methods. A 55-year-old Chinese woman was admitted to our hospital with diarrhea. She was diagnosed with Cronkhite-Canada Syndrome (CCS). The clinical symptoms of the patient included diarrhea, nausea, retching, anorexia, weight loss, and we found that she had alopecia, onychatrophy, rampant caries and skin pigmentation from the physical examination. Gastrointestinal endoscopy revealed multiple polyps in the gastric antrum, stomach body, ileocecal part and colon, and from the microscopically the polype hyperplsique was observed. The patient was treated by eradicating Helicobacter pylori and regulating the intestinal flora disbalance and his diarrhea improved within a short period of time. We suggested that she should take glucocorticoids orally, but the patient refused. Follow-up at 1 year showed that the symptoms of the patient had recurred sometimes, and she had taken Chinese herbal medicine orally a few times. At present, the symptoms of diarrhea are relieved, the weight of the patient has increased, and the hair and nails of the patient have grown again. From this case, we learned CCS can be likely ignored and not be diagnosed promptly because the low morbidity of CCS.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".