Source Control Review in Clinical Trials of Anti-Infective Agents in Complicated Intra-Abdominal Infections
Bibliographic record
Abstract
In clinical trials of complicated intra-abdominal infections, assessment of adequacy of the initial surgical approach to the management of the infection is of considerable importance in determining outcome. Antibiotic therapy would not be expected to adequately treat the infection if the surgical procedure was inadequate with respect to source control. Inclusion of such cases in an efficacy analysis of a particular therapeutic antibiotic may confound the results. We analyzed the source control review process used in double-blind clinical trials of antibiotics in complicated intra-abdominal infections identified through systematic review. We searched MEDLINE (PubMed) and ClinicalTrials.gov databases to identify relevant articles reporting results from double-blind clinical trials that used a source control review process. Eight prospective, randomized, double-blind, multicenter, clinical trials of 5 anti-infective agents in complicated intra-abdominal infections used a source control review process. We provide recommendations for an independent, adjudicated source control review process applicable to future clinical trials.
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 | 0.346 | 0.609 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".