Source control in intra-abdominal infections: What you need to know
Bibliographic record
Abstract
ABSTRACT: Providing optimal source control (SC) for intra-abdominal sepsis (IAS) is a critically important surgical principle, yet one that remains nebulous in terms of strict definitions and required conduct. The entire concept of SC has evolved in the last decades. Contemporary SC is not only surgical but also embraces minimally invasive percutaneous and medical therapies. We propose that adequate SC has evolved from the mere anatomical control of enteric leakage, cleansing of obvious contaminants and necrosis, to a more comprehensive anatomo-phyiological-biochemical model. While any breaches in the integrity of the gastrointestinal tract should be addressed urgently, SC should ultimately aim to control the generation and propagation of systemic biomediators, bacterial toxins, and toxic catabolites that perpetuate multisystem organ failure and death. Much urgently needs to be learned to understand and hopefully mitigate the dysbiotic influences of IAS on the human microbiome. Finally, the therapy offered should always be individualized, recognizing patient's unique pathophysiology, clinical condition, comorbidities, and predeclared preferences regarding invasive therapies and life-support.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".