Letter: what else can improve survival in cirrhotic patients with spontaneous bacterial peritonitis and associated septic shock? Authors’ reply
Bibliographic record
Abstract
We applaud the discussion points raised by Chen et al. in response to our recent article.1, 2 They raise the issue of why patients receiving appropriate anti-microbial therapy before the development of septic shock had a paradoxically lower survival rate (3/25, 12%). This is likely due to selection bias. To be included within this retrospective database, patients had to develop septic shock (hypotension). Hence, patients who were diagnosed with sepsis and received appropriate anti-microbial therapy, but did not become hypotensive, did not meet the entry criteria for the CATSS registry. In contrast, patients who got appropriate anti-microbials and subsequently developed shock likely failed appropriate anti-microbial therapy, and are a subset of patients with a poorer prognosis (87.5% mortality). Hence, only the ‘failures’ were identified. This phenomenon has been demonstrated in other studies from the CATTS registry. In a study of 2731 critically ill patients with septic shock, 21% of the patients (n = 577) failed appropriate anti-microbial therapy and progressed to septic shock, with similar results [survival vs. the overall group (52.2% vs. 58.0%)].3 In cirrhotics, it can be very difficult to differentiate between ‘liver-related deaths’ and ‘multiorgan failure’. For example, hepatorenal syndrome or acute kidney injury are a consequence of cirrhosis and spontaneous bacterial peritonitis, but lead to multiorgan failure.4 Unfortunately, the CATSS database was initially constructed as a registry for general critical care patients. While this granular data is not available, trying to dissect ‘liver related’ vs. ‘nonliver related’ is virtually impossible as shown by Moreau et al. in the CANONIC study.5 Finally, our high (100%) mortality rate in fungal peritonitis/shock is consistent with other studies.6 Prior studies have shown that directed anti-fungal therapy did not improve patient outcomes.7 Hence, fungal peritonitis may potentially be a poor prognostic marker. Given that we had 11 patients in our cohort, however, these are relatively small numbers to make any overarching recommendations for empirical anti-fungal therapy. The authors’ declarations of personal and financial interests are unchanged from those in the original article.2
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".