Neurogenic Bowel in Acute Rehabilitation Following Spinal Cord Injury: Impact of Laxatives and Opioids
Bibliographic record
Abstract
Objective: To explore the association between bowel dysfunction and use of laxatives and opioids in an acute rehabilitation setting following spinal cord injury (SCI). Methods: Data was collected regarding individuals with acute traumatic/non-traumatic SCI over a two-year period (2012–2013) during both the week of admission and discharge of their inpatient stay. Results: An increase in frequency of bowel movement (BM) (p = 0.003) and a decrease in frequency of fecal incontinence (FI) per week (p < 0.001) between admission and discharge was found across all participants. There was a reduction in the number of individuals using laxatives (p = 0.004) as well as the number of unique laxatives taken (p < 0.001) between admission and discharge in our cohort. The number of individuals using opioids and the average dose of opioids in morphine milligram equivalents (MME) from admission to discharge were significantly reduced (p = 0.001 and p = 0.02, respectively). There was a positive correlation between the number of laxatives and frequency of FI at discharge (r = 0.194, p = 0.014), suggesting that an increase in laxative use results in an increased frequency of FI. Finally, there was a significant negative correlation between average dose of opioids (MME) and frequency of BM at discharge, confirming the constipating effect of opioids (r = −0.20, p = 0.009).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".