The Impact of Various Platelet Indices as Prognostic Markers of Septic Shock
Bibliographic record
Abstract
INTRODUCTION: Platelet indices, including mean platelet volume (MPV), are readily available blood tests, although their prognostic value in patients with septic shock has not been fully explored. Current evidence has found contradictory results. This study aims to explore the behavior of platelet indices in septic shock and their clinical prognostic value. METHODS: Charts of septic shock patients from January to December 2012 in a tertiary medical center in Northern China were reviewed retrospectively. Platelet indices were recorded during the first five consecutive days after admission, as well as the penultimate and the last day of hospital stay. The data were compared between surviving and non-surviving patients. RESULTS: A total of 124 septic shock patients were enrolled. Thirty-six of the patients survived and 88 of them expired. MPV in the non-survivor group was higher than that of the survivor group, especially on the last day. PDW and PLCR showed increased trends, while PCT and PLT decreased in the non-survivor group. Among the PLT indices, MPV had the highest area under the receiver operating characteristic curve (0.81) with a precision rate of 75.6% at a cut-off of 10.5.Compared with other more usual septic shock prognostic markers, MPV is second only to lactate for the highest area under the curve. CONCLUSION: A statistically significant difference was seen between survivors and non-survivors for platelet indices which make them easily available and useful prognostic markers for patients in septic shock.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".