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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".