Is mechanical bowel preparation necessary to reduce surgical site infection following colon surgery? Protocol for a multicentre Canadian randomized controlled trial
Bibliographic record
Abstract
AIM: There is significant practice variation with respect to the use of bowel preparation to reduce surgical site infection (SSI) following colon surgery. Although intravenous antibiotics + mechanical bowel preparation + oral antibiotics (IVA + MBP + OA) has been shown to be superior to IVA + MBP and IVA, there are insufficient high-quality data from randomized controlled trails (RCTs) that directly compare these options. This is an important question, because if IVA + OA has similar effectiveness to IVA + MBP + OA, mechanical bowel preparation can be safely omitted, and the associated side effects avoided. The aim of this work is to compare rates of SSI following IVA + OA + MBP (MBP) versus IVA + OA (OA) for elective colon surgery. METHOD: This is a multicentre, parallel, two-arm, noninferiority RCT comparing IVA + OA + MBP versus IVA + OA. The primary outcome is the overall rate of SSI 30 days following surgery. Secondary outcomes are length of stay and 30-day emergency room visit and readmission rates. The planned sample size is 1062 subjects with four participating high-volume centres. Overall SSI rates 30 days following surgery between the treatment groups will be compared using a general linear model. Secondary outcomes will be analysed with linear regression for continuous outcomes, logistic regression for binary outcomes and modified Poisson regression for count data. CONCLUSION: It is expected that IVA + OA will work similarly to IVA + MBP + OA and that this work will provide definitive evidence showing that MBP is not necessary to reduce SSI. This is highly relevant to both patients and physicians as it will have the potential to significantly change practice and outcomes following colon surgery in Canada and beyond.
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 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 | 0.053 | 0.055 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".