Complications of dynamic graciloplasty
Bibliographic record
Abstract
PURPOSE: Dynamic graciloplasty can improve continence in patients with severe refractory fecal incontinence, but associated morbidity is high. The purpose of this study was to identify complications associated with dynamic graciloplasty and to characterize their treatment and impact on patient outcome. METHODS: In 121 patients enrolled in a prospective trial of 20 centers and eligible for safety analysis, all complications of dynamic graciloplasty were recorded at the time of their occurrence and followed up until resolution. Severe treatment-related complications were defined as those requiring hospitalization or surgical intervention. RESULTS: In 93 patients, 211 complications occurred. Of these, 89 (42 percent) in 61 patients were classified as severe treatment-related complications and resulted from the following: major infection, 19; minor infection, 10; thromboembolic events, 3; device performance and use, 13; pain, 16; noninfectious gracilis problems, 8; noninfectious wound-healing problems, 3; other surgery-related complications, 3. In addition, severe treatment-related complications resulted from constipation in ten and stoma creation or closure in ten. The recovery rate (full or partial) was 87 percent overall, and for severe treatment-related complications, was 92 percent. Of the types of complications, only major infections had an adverse effect on outcome. CONCLUSION: Severe complications occur frequently after dynamic graciloplasty, but are usually treatable. They often require one or more reoperations and can lead to significant delays in completion of therapy. In most cases therapy can be salvaged.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".