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Record W3110547181 · doi:10.1136/gutjnl-2020-iddf.93

IDDF2020-ABS-0147 Development of a validated nomogram to predict aggressive Crohn’s disease: a retrospective cohort study

2020· article· en· W3110547181 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAbstracts · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsNomogramMedicineRetrospective cohort studyReceiver operating characteristicCrohn's diseaseInternal medicineLogistic regressionOdds ratioArea under the curveCohortPredictive value of testsSurgeryPerforationDisease

Abstract

fetched live from OpenAlex

<h3>Background</h3> Predicting aggressive Crohn’s disease (CD) is crucial for determining therapeutic strategies. We aimed to develop a prognostic model to predict disease-related complications leading to early-onset surgery within 1 year after diagnosis of CD and to create a nomogram to facilitate clinical decision-making. <h3>Methods</h3> This retrospective study was conducted from January 1, 2012, to December 31, 2016, in a single tertiary referral center, using data from patients newly diagnosed with CD and showing B1 behavior according to Montreal classification. The model was established using multivariable logistic regression analysis with evaluation of the receiver operating characteristic (ROC) curves and areas under the curve (AUC). The model was calibrated and assessed for discrimination. Further, a user-friendly nomogram was created. <h3>Results</h3> The mean follow-up period was 53.45±12.81 months. Of 614 eligible patients, 13.5% developed surgery-related complications, including stenosis, perforation, and severe gastrointestinal bleeding. We identified age (Odds ratio (OR) 0.914, P=0.004), disease duration (OR 2.675, P&lt;0.001), perianal disease (OR 16.013, P&lt;0.001), previous surgery (OR 3.652, P=0.003), and extraintestinal manifestations (OR 7.625, P=0.001) as significant independent factors associated with early-onset complications and developed a prognostic model ((figure 1A), A Prognostic model predicting complications leading to surgery within 1 year after diagnosis), whose predictive ability was appraised with AUC of 0.965, specificity of 96.71%, and sensitivity of 67.24%. This model was validated with good discrimination (AUC of 0.933), and excellent calibration was demonstrated using the Hosmer-Lemeshow goodness-of-fit test ((figure 1B), Hosmer-Lemeshow goodness-of-fit test demonstrating a good fit of this model). A nomogram was created to facilitate clinical bedside practice ((figure 1C) A nomogram predicting complications leading to surgery within 1 year after diagnosis in Crohn’s disease patients). <h3>Conclusions</h3> This validated prognostic model can effectively predict early-onset complications leading to surgery and screen aggressive CD, enabling physicians to customize therapeutic strategies and monitor the intensive disease.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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