Evaluation of systemic inflammatory response syndrome-negative sepsis from a Chinese regional pediatric network
Bibliographic record
Abstract
BACKGROUND: The identification of sepsis in children varies depending on the definition used. Our purpose was to compare clinical data and outcome of atypical sepsis, manifested as having sepsis but not fulfilling the criteria of systemic inflammatory response syndrome (SIRS-negative sepsis, SNS), in children based on the modified Angus criteria with those of sepsis (S) and severe sepsis (SS) based on the international consensus criteria. METHODS: Pediatric departments in 11 regional city and county referral hospitals with emergency and intensive care settings in Huai'an serving for 843,000 children participated in a parallel multicenter prospective survey. Clinical data registry was used to recruit those who fulfilled the diagnostic criteria for pediatric sepsis from all admissions (n = 27,836) from 28 days to 15 years old, from September 1, 2010 to August 31, 2011. RESULTS: A total of 1606 children met the criteria for pediatric sepsis and were divided into three groups: S, (n = 1377), SS (n = 153, including 32 septic shock), based on the consensus definition criteria, and SNS (n = 76) based on the modified Angus criteria. Most deaths (38/54, 70.3%) occurred within three days of admission. The SNS mainly occurred in infants and was associated with cardiopulmonary and neurologic dysfunction without satisfying the SIRS criteria. CONCLUSIONS: SNS differed from SS in that it predominantly affected infants and manifested with cardiopulmonary and neurologic dysfunction. There were no laboratory variables which were useful in identification of SNS, or predicting response to therapy or outcome.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".