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Interstitial cells of Cajal in health and disease. Part I: Normal ICC structure and function with associated motility disorders

2007· review· en· W2033538743 on OpenAlexaff
Catherine Streutker, J D Huizinga, David K. Driman, Robert H. Riddell

Bibliographic record

VenueHistopathology · 2007
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsMount Sinai HospitalLondon Health Sciences CentreMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsInterstitial cell of CajalMotilityBiologyInterstitial cellPathologyCell biologyCell typeNeuroscienceStem cell factorTransmembrane proteinReceptorAnatomyCellStem cellImmunohistochemistryImmunologyMedicineGeneticsHaematopoiesis

Abstract

fetched live from OpenAlex

Ramon y Cajal (1852-1934) is considered to be one of the founders of the field of neuroscience. In 1911, he described interstitial neurons in the gut, noting that they were primitive accessory components that perhaps modify smooth muscle contraction, themselves subject to regulation from principle neurons. The accuracy of his description of their appearance and activities has led to these cells now being called the interstitial cells of Cajal (ICC). Thuneberg and Faussone-Pellegrini were instrumental in bringing these cells to the attention of gastroenterologists and pathologists in the early 1980s. Subsequently, the development of antibodies to c-kit has allowed routine identification of the ICC in pathology specimens. c-Kit is a transmembrane protein kinase which has as ligand stem cell factor and is involved in cell development in a variety of cell lineages. In the gut musculature, ICC and mast cells are the only cells that have prominent c-kit expression. The ICC are now known to play an important role in gut motility and absent or disordered ICC networks have been identified in a variety of motility disorders.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.023
GPT teacher head0.286
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations136
Published2007
Admission routes1
Has abstractyes

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