PTH-089 Rates of wound healing in patients with Crohn’s disease undergoing proctectomy
Bibliographic record
Abstract
Introduction About 20% patients with perianal Crohn’s (pCD) undergo proctectomy with a significant number developing unhealed wounds. The purpose of this study was to determine factors which may be associated with poor wound healing in patients with pCD who had undergone proctectomy in the biologics era. Methods Case record review was carried out of 79 patients with pCD who underwent proctectomy at St Mark’s Hospital between 2005 and 2017. Healing rates at 6 and 12 months post proctectomy were considered and univariate regression analysis was performed. Results Complete data regarding healing were available for 97.5% (77/79) at 6 months and 100% at 12 months. 45/77 (43.7%) patients had failure of wound healing at 6 months and 34/79 (33%) at 12 months. A younger age at diagnosis of Crohn’s disease was significantly associated with failure of healing at 12 months (median age 21± 9.7 unhealed; median age 27 ± 13.6 healed; p=0.03). 76.7% (61/79) patients received biologic treatments prior to proctectomy, however exposure to biologics was not a significant factor in predicting failure of wound healing (Infliximab p=0.74; Adalimumab p=0.57; Vedolizumab p=0.21). Current smoking status was not associated with poor wound healing (p=0.18). Other parameters which were not associated with failure of wound healing in our cohort included gender, corticosteroid exposure in the previous one month, thiopurine exposure in previous 3 months, number of biologics exposed to, perianal sepsis on MRI within the last 12 months, Montreal Classification, duration of CD prior to proctectomy, albumin and CRP. Conclusion A third of patients have unhealed wounds after 1 year follow-up after protectomy. A younger age at diagnosis of Crohn’s disease was the only factor associated with an unhealed perineal wound; this may in part be due to more severe disease progression in patients diagnosed at a younger age. Larger scale studies are required to determine if other parameters such as exposure to biologics play a role in predicting rates of wound healing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".