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P-104 SECURE

2016· article· en· W2323159565 on OpenAlexaff
Scott Lee, Doug Wolf, Brian G. Feagan, Gary R. Lichtenstein, Elizabeth Andrews, Ryan Stidham, Faten Aberra, Humberto Aguilar, Cem Kayhan, Gordana Kosutic, David Sen, Amanda Golembesky, Iram Hasan, Marshall Spearman, Edward V. Loftus

Bibliographic record

VenueInflammatory Bowel Diseases · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsRobarts Clinical Trials
Fundersnot available
KeywordsMedicineCohortCertolizumab pegolAdverse effectInternal medicineIncidence (geometry)Cohort studyPediatricsSurgeryDiseaseAdalimumab

Abstract

fetched live from OpenAlex

Tumor necrosis factor-alpha antagonists are associated with an increased risk of serious infections and some malignancies. The objective of the SECURE registry is to evaluate these safety outcomes in patients with Crohn's disease (CD) treated with certolizumab pegol (CZP) versus other CD treatments. SECURE is a long-term registry of patients with CD. Patients were enrolled in either the CZP cohort (those who had received CZP for ≤12 months or were about to receive CZP at enrollment) or the comparison cohort (CO; those receiving corticosteroids, immunosuppressants, or non-CZP biologics). The cutoff date for this interim analysis was March 31, 2014. Exposure time for all events, excluding malignancies, took into account treatment switching. Observational time was divided into 4 mutually exclusive categories: CZP cohort (exposure to CZP only); CO cohort (exposure to other CD treatments); Overlap cohort (exposure overlap between CZP and other CD treatments); and Gap cohort (no exposure to any CD treatment). Incidence rates and 95% confidence intervals (CIs) per 100 patient-years were calculated for adverse events of interest. Serious adverse events (SAEs) were summarized as percentages of patients reporting ≥1 SAE per system organ class. Owing to the longer latency, the exposure times for malignancies were calculated differently and reported elsewhere.1 A total of 2453 (1097 CZP; 1356 CO) patients were enrolled; 1464 patients (59.7%) were ongoing and 989 patients (40.3%) had discontinued at cutoff. At enrollment, the mean age of patients was 40.6 years (range, 7.3–88.6); more patients had moderate to severe or severe disease in the CZP versus CO cohort (moderate to severe, 40.7% versus 20.1%; severe, 9.6% versus 2.7%) and the proportion of patients in remission was higher for CO versus CZP (37.6% versus 9.6%). Planned exposure for all patients based on cohort assignment at enrollment was 5868.8 patient-years (2477.7 and 3391.1 patient-years for CZP and CO, respectively). When accounting for treatment switching, actual exposure to CZP only was 601.1 patient-years; 3465.9 patient-years to CO only; 705.1 patient-years to concurrent CZP/CO exposure; and 1096.8 patient-years with a gap in exposure. The incidence rates of serious infections were: CZP only, 0.35 (95% CI, 0.04–1.25); CO only, 0.53 (95% CI, 0.32–0.84); Overlap, 1.33 (95% CI, 0.61–2.53); and Gap, 0.34 (95% CI, 0.07–0.99). The percentage of patients with SAEs was higher in the CZP cohort versus CO versus Overlap versus Gap for metabolism/nutrition disorders (1.2% versus 0.7% versus 0.9% versus 0.6%) and general disorders/administration site conditions (1.0% versus 0.5% versus 0.3% versus 0.8%), but lower for gastrointestinal disorders (7.0% versus 9.4% versus 14.0% versus 6.3%), infections (2.2% versus 4.3% versus 6.4% versus 2.2%), and surgical/medical procedures (1.3% versus 2.2% versus 1.3% versus 1.0%). These interim results showed that the incidence of infections during CZP treatment was similar to that with other CD treatments. Although the number of patient-years at risk in this analysis was relatively small, these data are consistent with the established CZP safety profile.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.477
Threshold uncertainty score0.746

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4770.249

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 head0.005
GPT teacher head0.221
Teacher spread0.215 · 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.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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