Expert consensus on the monitoring and treatment of sepsis-induced immunosuppression
Bibliographic record
Abstract
Emerged evidence has indicated that immunosuppression is involved in the occurrence and development of sepsis. To provide clinical practice recommendations on the immune function in sepsis, an expert consensus focusing on the monitoring and treatment of sepsis-induced immunosuppression was developed. Literature related to the immune monitoring and treatment of sepsis were retrieved from PubMed, Web of Science, and Chinese National Knowledge Infrastructure to design items and expert opinions were collected through an online questionnaire. Then, the Delphi method was used to form consensus opinions, and RAND appropriateness method was developed to provide consistency evaluation and recommendation levels for consensus opinions. This consensus achieved satisfactory results through two rounds of questionnaire survey, with 2 statements rated as perfect consistency, 13 as very good consistency, and 9 as good consistency. After summarizing the results, a total of 14 strong recommended opinions, 8 weak recommended opinions and 2 non-recommended opinions were produced. Finally, a face-to-face discussion of the consensus opinions was performed through an online meeting, and all judges unanimously agreed on the content of this consensus. In summary, this expert consensus provides a preliminary guidance for the monitoring and treatment of immunosuppression in patients with sepsis.
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 | 0.144 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".