A new look at toxic megacolon: an update and review of incidence, etiology, pathogenesis, and management
Bibliographic record
Abstract
Toxic megacolon (TM) is an infrequent but devastating complication of colitis. Numerous forms of colonic inflammation can give rise to TM but the majority occur in individuals with inflammatory bowel disease (IBD). Recently there has been a marked increase in the number of reports of TM associated with pseudomembranous colitis. Because of the associated high morbidity and mortality, early recognition and management of TM is of paramount importance. The mechanisms involved in development of TM are not clearly delineated, but chemical mediators such as nitric oxide and interleukins may play a pivotal role in the pathogenesis. New evidence suggests that TM and its associated morbidity may be predicted by the extent of small bowel and gastric distension in patients with colitis. CT scanning may also play an important role the management of TM, in that it may be the only noninvasive mode to detect subclinical perforations and abscesses. Management involves close medical attention, supportive care, and treatment of the underlying colitis. Possible exacerbating factors such as narcotic and anticholinergic medications must be withdrawn, and colonic decompression via tube drainage or positional techniques must be considered. Signs of progression or complications of the disease must be treated aggressively with surgical intervention, as delay is associated with even greater risk of mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".