Comparison of Stress-Hemoconcentration Correction Techniques for Stress-Induced Coagulation
Bibliographic record
Abstract
When examining stress effects on coagulation, arithmetic correction is typically used to adjust for concomitant hemoconcentration but may be inappropriate for coagulation activity assays. We examined a new physiologically relevant method of correcting for stress-hemoconcentration. Blood was drawn from healthy men (N = 40) during baseline, mental stress, and recovery, and factor VII activity (FVII:C), factor VIII activity (FVIII:C), activated partial thromboplastin time (APTT), prothrombin time (PT%), fibrinogen, D-dimer, and plasma volume were determined. Three hemoconcentration correction techniques were assessed: arithmetic correction and two reconstitution techniques using baseline plasma or physiological saline. Area-under-the-curve (AUC) was computed for each technique. For FVII:C, uncorrected AUC was significantly greater than AUC corrected arithmetically. For PT%, uncorrected AUC was significantly greater than AUC corrected with saline or arithmetically. For APTT, uncorrected AUC was significantly less than AUC corrected with saline and greater than AUC corrected arithmetically. For fibrinogen, uncorrected AUC was significantly greater than AUC corrected with saline or arithmetically. For D-dimer, uncorrected AUC was significantly greater than AUC corrected arithmetically. No differences in AUC were observed for FVIII:C. Saline reconstitution seems most appropriate when adjusting for hemoconcentration effects on clotting time and activity. Stress-hemoconcentration accounted for the majority of coagulation changes.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".