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Record W4294573012 · doi:10.1016/j.amsu.2022.104090

Cronkhite-Canada syndrome: A case report and review of the literature

2022· article· en· W4294573012 on OpenAlexaboutno aff
Mingxiao Ma, Yaochan Huang, Zhimin Suo, Xuhui Ma

Bibliographic record

VenueAnnals of Medicine and Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiarrheaWeight lossNauseaAnorexiaRetchingVomitingStomachSurgeryInternal medicineGastroenterologyDermatologyObesity

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.311
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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