How to Avoid False-Negative and False-Positive Diagnoses of Platelet Apoptosis: Illustrative Experimental and Clinically Relevant Cases
Bibliographic record
Abstract
Platelets may selectively execute apoptosis (PL-Apo), activation (PL-Act), and both or no responses when exposed to different chemical agents, shear stresses, and stored under blood banking conditions. Appropriate diagnosis of PL-Apo is an important issue of platelet physiology investigations. However, in diagnosing PL-Apo, there is a risk of a false-negative or false-positive diagnosis. The goal of the current review is to present recommendations that may help to avoid incorrect PL-Apo diagnosis. Analyzing reported studies, we recommend (1) using platelet-rich plasma rather than isolated platelets to minimize artificial stimulation of PL-Apo during platelet isolation, (2) using established optimal conditions for stimulation of PL-Apo and/or PL-Act, (3) using a panel of PL-Apo and PL-Act markers, and (4) appropriate positive and negative controls for quantification of PL-Apo and PL-Act responses.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".