Optimal treatment for severe neurogenic bowel dysfunction after chronic spinal cord injury: a decision analysis
Bibliographic record
Abstract
BACKGROUND: When conservative management fails in patients with chronic spinal cord injury (SCI) and neurogenic bowel dysfunction, clinicians have to choose from a variety of treatment options which include colostomy, ileostomy, Malone anterograde continence enema (MACE) and sacral anterior root stimulator (SARS) implantation. This study employed a decision analysis to examine the optimal treatment for bowel management of young individuals with chronic refractory constipation in the setting of chronic SCI. METHODS: A decision analysis was created to compare the four surgical strategies using baseline analysis, one-way and two-way sensitivity analyses, 'worst scenario' and 'best scenario' sensitivity analyses, and probabilistic sensitivity analyses. Quality-adjusted life expectancy (QALE) was the primary outcome. RESULTS: The baseline analysis indicated that patients who underwent the MACE procedure had the highest QALE value compared with the other interventions. Sensitivity analyses showed that these results were robust. CONCLUSION: The MACE procedure may provide the best long-term outcome in terms of the probability of improving bowel function, reducing complication rates and the incidence of autonomic dysreflexia, and being congruent with patients' preferences. The analysis was sensitive to changes in assumptions about quality of life/utility, and thus the results could change if more specific estimates of utility became available.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".