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Record W4221067578 · doi:10.4103/sjg.sjg_3_22

Therapeutic drug monitoring for biological medications in inflammatory bowel disease

2022· review· en· W4221067578 on OpenAlexaff
Rachel C. Cogan, Basem W. El-Matary, Wael El‐Matary

Bibliographic record

VenueSaudi Journal of Gastroenterology · 2022
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineTherapeutic drug monitoringInflammatory bowel diseaseDrugIntensive care medicineVedolizumabTrough levelDiseaseImmunogenicityPharmacologyImmunologyInternal medicineAntibody

Abstract

fetched live from OpenAlex

Therapeutic drug monitoring (TDM) is the measurement of serum drug concentrations and anti-drug-antibodies (ADA) for biologic therapies used to treat inflammatory bowel disease (IBD). The aim of this article is to review the current literature concerning reactive and proactive TDM for both adults and children with IBD. Although optimal trough concentration windows for some of these medications are not well defined, there is mounting evidence to suggest that reactive TDM is associated with favorable therapeutic outcomes, including less immunogenicity, greater drug exposure, and a decreased risk of treatment failure. Moreover, while the exact mechanism of loss of response is not fully elucidated, the vast majority of studies have reported a decreased incidence of nonresponse and secondary loss of response when TDM is implemented. Proactive TDM, while even less understood in the literature, employs a schedule of preemptive analysis of serum trough concentrations to accordingly adjust the patient's biologic dosage. Proactive TDM may decrease the need for IBD-related surgery/hospitalization, and therefore merits future studies of investigation.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.984
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.132
GPT teacher head0.427
Teacher spread0.295 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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