Response to Letter, “Risk of Venous Thromboembolism After Hospital Discharge in Patients With Inflammatory Bowel Disease”
Bibliographic record
Abstract
We thank Dr. Dai and colleagues for their interest in our study, which compared the risk of venous thromboembolism (VTE) after hospital discharge in patients with inflammatory bowel disease (IBD) and in non-IBD control patients. We agree with the authors that the pathogenesis of VTE is multifactorial. One of the strengths of our study was our ability to control for a large number of variables that have been associated with VTE risk.1-3 Contrary to the authors’ assertion, we had a near-perfect match in baseline variables in our nonsurgical cohorts, based on our propensity score, including prior history of VTE and requirement for central venous catheters. Although there were subtle differences in age and comorbidities between our surgical cohorts, each of these variables was subsequently adjusted in our Cox proportional hazard models. Therefore, it is unlikely that differences in these variables accounted for our observations. Although the use of health administrative data has significant advantages, including the large sample size, the ability for longitudinal follow-up beyond hospitalization, and the availability of the full population of IBD patients in our region, there were limitations. The additional variables suggested by the authors as potential confounders were not available in our data. We were not able to determine clinical characteristics or genetic susceptibility. However, these were unlikely to have impacted our findings. For example, hereditary thrombophilia and other genetic variants associated with VTE are unlikely to differentially affect patients with IBD compared with non-IBD control patients. This result has been shown in a number of epidemiological studies where the rates of common genetic variants associated with VTE risk were comparable in patients with IBD and in the general population.4 Furthermore, fluid depletion during hospitalization is unlikely to directly impact VTE risk after hospital discharge given that fluid deficits are normally corrected before discharge. We acknowledge that there were a number of variables that we were unable to capture from our administrative data that may impact VTE risk, such as smoking and obesity. However, considering our population-based sample and matching of IBD patients and non-IBD control patients, it is unlikely that IBD patients would be at differential risk. Finally, we were unable to determine IBD severity or the use of medications, which are not available for all residents in our province. Although we agree with Dr. Dai and colleagues that these 2 variables have been associated with VTE,2,5,6 our study compared IBD patients with non-IBD control patients and was not designed to determine which factors were responsible for differences in the rates of VTE between these 2 populations. Conflicts of interest: JM reports consultancy fees and/or honoraria from Jannsen, AbbVie, Takeda, and Pfizer.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.041 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 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".