Prediction of ICU Admission and its Outcome a Prospective Study of Different Scoring Systems in Women with Pregnancy Associated Sepsis
Bibliographic record
Abstract
Objective: This study aims to compare the effectiveness of the Sequential Organ Failure Assessment (SOFA) and the Sepsis in Obstetrics Score (SOS) in predicting admission to intensive care and mortality in pregnant women with pregnancy-associated sepsis (PAS). Specifically, the researchers wanted to determine the performance of these two scoring systems. Methods: Cases were recruited from the obstetrics department who were diagnosed with PAS and met any two of the criteria for fast SOFA (qSOFA). At the time of admission, the features of SOFA and SOS were recorded and compared to determine the influence of these two models on patient outcomes. Place of Study: Hayat memorial teaching hospital Duration of Study: January 2021 to May 2022 Results: There were 30 intensive care patients, which leads to a significant fatality rate (31.7%). This was associated with the deaths of numerous patients. A criteria of SOFA less than 6 had the optimal combination of sensitivity (84.4%) and specificity (61,3%) for determining critical care admission for the study population. A cutoff value less than six produced the highest levels of sensitivity (64%) and specificity (40%) for the same. Conclusions: Compared to SOS, SOFA produced a significantly more accurate forecast of both the patient's dire health and the likelihood of their death. SOFA performed significantly better than SOS when assessing the proportion of PAS patients who required critical care hospitalization and the death rate. Keywords: SOFA • SOS • Obstetric sepsis
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".