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Record W2153157336 · doi:10.4021/gr2009.07.1305

Common Pitfalls in Management of Inflammatory Bowel Disease

2009· review· en· W2153157336 on OpenAlexvenueno aff
Lakshmi Pasumarthy

Bibliographic record

VenueGastroenterology Research · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntensive care medicineInflammatory bowel diseaseMEDLINEInflammatory Bowel DiseasesHealth careDiseaseDisease managementAlternative medicineInternal medicinePathologyHealth management system

Abstract

fetched live from OpenAlex

Our understanding of inflammatory bowel disease (IBD), treatment options, complications and their management has expanded significantly over the past few decades. When caring for patients it is important to remember the complexities of pathogenesis and pharmacology. This review is to identify errors in diagnosis, treatment, complications and preventive care issues that arise while caring for patients with IBD and to provide recommendations and information that can be shared with patients and their health care providers. A review of the literature was undertaken using MEDLINE from 1981 to present. We included randomized controlled studies, case-control studies, and review articles. There are many associated conditions and complications recognized in patients with IBD and current treatment strategies do result in many side effects, some are serious and some are not widely recognized. With the advent of anti-TNF therapies and the newer 5-amino salicylate derivatives, options available have increased significantly. It is also important to remember that these patients are followed by more than one health care provider and it is important for all involved to communicate the plan of action.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.036
GPT teacher head0.365
Teacher spread0.328 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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