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Record W2015381087 · doi:10.4236/crcm.2014.312138

Cronkhite-Canada Syndrome: A Case Report and Literature Review of Gastrointestinal Polyposis Syndrome

2014· article· en· W2015381087 on OpenAlexaboutno aff
Xinying Shen, Michael Husson, William Lipshutz

Bibliographic record

VenueCase Reports in Clinical Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEnteropathyEtiologyDermatologyHyperpigmentationGastroenterologyPathologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Cronkhite-Canada syndrome (CCS) is a rare, non-inherited polyposis syndrome, characterized by diffuse gastrointestinal (GI) hamartomatous polyposis with unique dermatologic changes including alopecia, skin hyperpigmentation, and nail dystrophy. Patients can typically present with diarrhea, weight loss, protein-losing enteropathy, and nutritional deficiency. However, it can demonstrate diverse other clinical features, usually with poor prognosis. Currently, there are no specific diagnostic criteria established yet. The etiology of CCS is still obscure, but an autoimmune process has been suggested. Here we present an 81-year-old Caucasian female who had clinical presentations, physical exam, imaging, endoscopy and pathology findings that were all consistent with the diagnosis of CCS. We also include a detailed literature review of the other gastrointestinal polyposis syndromes (hamartomatous, adenomatous, hyperplastic and inflammatory polyposis). A high index of suspicion and recognition of the characteristic clinical, endoscopic as well as histopathological findings of CCS, as well as different gastrointestinal polyposis conditions, can help clinicians with more timely and correct diagnosis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.027
GPT teacher head0.350
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCase Reports in Clinical MedicineSame topicGenetic factors in colorectal cancerFrench-language works237,207