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Evolving Treatment Algorithms in Crohn's Disease

2016· review· en· W2413413511 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Drug Targets · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsHôpital Charles-Le MoyneUniversité de Sherbrooke
Fundersnot available
KeywordsCrohn's diseaseDiseaseMedicineIntensive care medicineInflammatory bowel diseaseTherapeutic approachNatural historyFistulaQuality of life (healthcare)AlgorithmSurgeryInternal medicineComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Crohn's disease (CD) is a chronic, disabling and destructive condition. Half of patients will develop some bowel damage (stricture, fistula and/or abscess). Current therapeutic strategies failed to alter its natural history. OBJECTIVE: We explore in a review article the evolution of CD treatment over a quarter of a century from a linear sequence of treatment intensification to a complex algorithm focused on individualized patient care by looking beyond symptoms. Specifically we focus on evolving concepts in assessing disease severity, selecting rigorous treatment end-targets, initiating an effective therapeutic therapy, and managing secondary loss of response. RESULTS: A tight monitoring of objective signs of inflammation and a treat-to-target approach are probably the only way to change patients' life and disease course. We now seek to optimize our therapeutic tools according to patient profile, disease phenotype and the unique pharmacodynamics that ensues. CONCLUSION: Standardizing the clinical practice of gastroenteroogists with the most current treatment algorithm may minimize disease related complications while favouring patient's quality of life.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
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 head0.025
GPT teacher head0.324
Teacher spread0.300 · 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