An Algorithm for Early Detection of Sepsis Using Traditional Statistical Regression Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sepsis is the final common pathway for many infections, whereby the body’s immune response leads to organ failure, and eventually death. It is associated with high mortality rates and, if survived, significant morbidity. Early detection is imperative to improve outcomes. Yet, there is also a need to avoid a high false alarm rate. The aim of this study was to develop and evaluate a simple algorithm for early sepsis detection.Significant missing data were encountered in the dataset, which were forward-filled or substituted with population means. Clinically relevant variable combinations were added along with transformation features including dichotomization, z-scores, first derivative, and changes from baseline. A logistic regression model was used to identify candidate features and build the overall risk score function for prediction.The final candidate score had areas under the receiver operating characteristic curve of 0.747, 0.760, and 0.783 for the three test data sets. It had accuracies of 0.795, 0.889, 0.815, respectively, and an overall utility score for the full test set of 0.249 using a cutoff of 0.024.Evaluation indicated significant potential for further optimization, including reduction of false-positive predictions. Adding features capturing change over time is expected to provide scope for further investigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it