Biological Treatment and the Potential Risk of Adverse Postoperative Outcome in Patients With Inflammatory Bowel Disease: An Open-Source Expert Panel Review of the Current Literature and Future Perspectives
Bibliographic record
Abstract
Abstract Background There is widespread concern that treatment with biologic agents may be associated with suboptimal postoperative outcome after surgery for inflammatory bowel diseases (IBD). Aim We aimed to search and analyze the literature regarding the potential association of biologic treatment on adverse postoperative outcome in patients with IBD. We used the subject as a case in point for surgical research. The aim was not to conduct a new systematic review. Method This is an updated narrative review written in a collaborative method by authors invited through Twitter via the following hashtags (#OpenSourceResearch and #SoMe4Surgery). The manuscript was presented as slides on Twitter to allow discussion of each section of the paper sequentially. A Google document was created, which was shared across social media, and comments and edits were verified by the primary author to ensure accuracy and consistency. Results Forty-one collaborators responded to the invitation, and a total of 106 studies were identified that investigated the potential association of preoperative biological treatment on postoperative outcome in patients with IBD. Most of these studies were retrospective observational cohorts: 3 were prospective, 4 experimental, and 3 population-based studies. These studies were previously analyzed in 10 systematic/narrative reviews and 14 meta-analyses. Type of biologic agents, dose, drug concentration, antidrug antibodies, interval between last dose, and types of surgery varied widely among the studies. Adjustment for confounders and bias control ranged from good to very poor. Only 10 studies reported postoperative outcome according to Clavien–Dindo classification. Conclusion Although a large number of studies investigated the potential effect of biological treatment on postoperative outcomes, many reported divergent results. There is a need for randomized controlled trials. Future studies should focus on the avoiding the weakness of prior studies we identified. Seeking collaborators and sharing information via Twitter was integral to widening the contributors/authors and peer review for this article and was an effective method of collaboration.
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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 | 0.030 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".