A novel role of prognostic nutritional index in predicting the effectiveness of infliximab in Crohn's disease
Bibliographic record
Abstract
Objective To investigate the predictive value of the prognostic nutritional index (PNI) for the effectiveness of infliximab (IFX) in patients with Crohn's disease (CD).Methods All data were retrospectively collected from Xiangya Hospital, Central South University between January 2016 and September 2021. Clinical remission at 52 weeks is the primary endpoint.Results Altogether, 193 CD patients were enrolled. PNI can identify clinical remission (p = 0.004), and the optimal cut-off value of the PNI was 39.2. 92/116 (79.3%) and 44/77 (57.1%) in the high- and low-PNI groups were in clinical remission at week 52 (p = 0.001). Patients with low PNI have poor general health at baseline. The body mass index, hemoglobin, platelet (PLT), serum creatinine, fibrinogen, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), Crohn's disease activity index (CDAI), and location of disease significantly differed between the two groups (p < 0.05). PNI was negatively correlated with CRP, ESR, PLT and CDAI (p < 0.05). The lower PNI, smoking history, and higher CDAI at baseline were the independent risk factors of disease activity at 52 weeks (p < 0.05). The high-PNI group is less likely to develop poor outcomes (p = 0.033).Conclusion The PNI may serve as a novel and promising biomarker in predicting the effectiveness of IFX and contribute to targeted management in CD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".