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Record W2597203545 · doi:10.30699/ijp.2017.23913

Malignant Colorectal Polyps; Pathological Consideration (A review)

2017· article· en· W2597203545 on OpenAlexaff
Bita Geramizadeh, Mahsa Marzban, David Owen

Bibliographic record

VenueIranian journal of pathology · 2017
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineSubmucosaMalignancyPathologicalColonoscopyLymphovascular invasionPathologyGeneral surgeryColorectal cancerInternal medicineCancerMetastasis

Abstract

fetched live from OpenAlex

BACKGROUND: Routine screening colonoscopy is on the rise and pathologists have to deal with the ever larger numbers of excised colonic polyps. It is very important to optimize the patients' individual treatment and further surveillance. Pathologists play a critical role in management, as most of the clinical decisions concerning colonic polyp management are based on pathologic findings. One of the most important clinical issues in colonic adenomas is the diagnosis of malignancy and reporting its different aspects by the pathologist. The histologic type and the extent of carcinoma within a malignant polyp have considerable impact on the decisions of gastroenterologists and surgeons for further management. Therefore, the most recent literature regarding the diagnosis and reporting of the different features of malignant polyps was reviewed. DATA ACQUISITION: There is growing literature regarding the different pathologic features and reporting of malignant colonic polyps, and in this review, published articles that are listed on Google Scholar and Pub Med are discussed. CONCLUSION: Diagnosis of malignant colon polyp requires the presence of tumor cells that are penetrating beyond the muscular mucosa into submucosa (pT1). As well as establishing a diagnosis of malignant polyp, it is very important to report the size of the invasive component, the presence or absence of lymphovascular invasion, the degree of tumor differentiation and the distance of the carcinoma from the line of resection. Other important features that may be reported include: the presence or absence of tumor budding, the depth of tumor cell penetration into the submucosa, and results of immunohistochemistry for mismatch repair proteins and BRAF.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.373

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 head0.039
GPT teacher head0.325
Teacher spread0.286 · 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 designObservational
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

Citations7
Published2017
Admission routes1
Has abstractyes

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