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Record W4391165150 · doi:10.1093/ecco-jcc/jjad212.0034

OP34 Risk of Disease Recurrence and Re-resections in Crohn's Disease Patients Undergoing Primary Bowel Resection: A Population-Based Study

2024· article· en· W4391165150 on OpenAlexaboutno aff
Anja Poulsen, Jim Rasmussen, Mads Damsgaard Wewer, Esben Holm Hansen, Rie Louise Møller Nordestgaard, Hans Søe Riis Jespersen, D Christiansen, Viviane A. Lin, Elena Surnacheva, Numan Ali Aydemir, Kari Anne Verlo, Frederik Rønne Pachler, Pernille Dige Ovesen, Kristian Asp Fuglsang, Christopher F. Brandt, Lars Tue Sørensen, Ismail Gögenür, Peter‐Martin Krarup, Johan Burisch, Jakob Benedict Seidelin

Bibliographic record

VenueJournal of Crohn s and Colitis · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationColonoscopyCohortCrohn's diseaseSurgeryCalprotectinInflammatory bowel diseaseBowel resectionInternal medicineDiseaseColorectal cancerCancer

Abstract

fetched live from OpenAlex

Abstract Background The rate of having a resection for patients with Crohn's disease (CD) have decreased over decades, while the rates of re-resections seem to have been stable around 1/3 in historic cohorts. Re-resection rates might be influenced by targeted therapies. We aimed to investigate the re-resection rates and risk of recurrence in a contemporary cohort and the effect of medical treatment on these parameters. Methods This population-based cohort included all CD patients undergoing primary intestinal resection between 2010 and 2020 in Eastern Denmark, with a background population of 2,730,000 (46% of the Danish population). Individual clinical characteristics, medication, surgical procedures, and complications, as well as imaging, and endoscopy results were collected. Disease recurrence was defined as a colonoscopy with SES-CD≥3, Rutgeerts score ≥2i, inflammation or stenosis on imaging (MRI, CTA, or IUS), fecal calprotectin ≥ 250 mg/kg, starting corticosteroids after resection or re-resection due to disease activity. We characterized the cohort using nonparametric statistics (median, IQR, percentages) and survival analysis. A Cox regression analysis with propensity score incorporating Montreal classification, age, gender, smoking, and types of resection (colon, ileocecal, or small bowel) was conducted to assess the effect of initiating prophylactic biologic treatment within the first year from resection. Results A total of 631 patients had a primary resection due to disease activity, with a median follow-up from time from diagnosis of 118 months (IQR: 69-170) (Table 1). Prior to the first resection 337 (53%) patients received immunomodulators and 249 (39%) biologics; the same numbers post-surgery were 314 (50%) and 264 (42%), respectively. A total of 256 (41%) patients were resected within two years from diagnosis while this proportion increased to 424 (67%) and 533 (84 %) after 5 and 10 years, respectively. Re-resection rates due to disease activity 5 and 10 years after primary resection were 5% and 10%. The median time from primary resection to disease recurrence was 11,3 months (IQR: 4.7-24.8) (Figure 1). Median time from first to second and second to third resection were 37 months (IQR: 15-64) and 38 months (IQR: 26-55), respectively. We found no significant difference regarding prophylactic biologic therapy within the first year of resection (n 45) based on recurrence or re-resection compared to patients not starting biologics within the first year (HR 0.40, 95%CI(0.12-1.34), p=0.14). Conclusion Most patients undergoing primary resection face post-surgery disease recurrence, yet 1 in 4 remain relapse-free for decades. Despite extensive biologic therapy, 15% still require further resections due to disease activity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.255
Teacher spread0.248 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

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