Predicting Surgery at the Moment of Diagnosis of Crohnʼs Disease Is Possible: Proposal of a Predictive Model
Bibliographic record
Abstract
Introduction: It is estimated that 80% of patients with Crohn's Disease (CD) will required a surgery anytime during their lives. However, few studies to determine predictors of surgery at the precise moment of diagnosis have been performed. We aimed to identify predictive factors for surgery at the moment of diagnosis and to propose a predictive model for that outcome, able to guide therapeutic decisions. Methods: Unicentric, retrospective, case-control study of CD patients: 71 cases with previous abdominal surgery for CD and 205 non-operated CD controls. Demographic, clinical, laboratorial, and endoscopic data and Montreal classification at the moment of diagnosis were collected. Statistical analysis was performed using SPSS software, version 22.0. Results: There were no differences between groups regarding gender (p=0,984) or family history (p=0,970). Smoking habits were more common in cases (p=0,003). Concerning Montreal classification, L3 location was more common in cases (pdiagnosis, anemia (p(cirurgia)=1/(1+e-[-1.692+1.1xL3+3.09xB2/B3-0.317xHb(g/dL)+0.214xLeuc(x10ˆ3/μL)])), with a specificity of 93.5%, a sensitivity of 71.4%, a positive predictive value of 76.9%, and a negative predictive value of 91.6%. Conclusion: At the moment of diagnosis, behaviour and location of CD and haemoglobin and leukocyte values, when integrated in the proposed predictive model, allow, with high specificity, identification of patients potentially requiring surgery. In these patients, the use of more aggressive medical therapies should be considered to avoid or at least defer a surgical outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".