High PLC-C level in major depressive disorder and its relationship with disease severity: a different perspective on coagulation in major depressive disorder
Bibliographic record
Abstract
Introduction: Large platelets are an important risk factor for the development of thrombosis.In this study, we aimed to measure the presence of large cell platelets and its relationship with disease severity in major depressive disorder (MDD).Material and methods: In this study, platelet volume indices were analyzed from the complete blood count (CBC) results of 103 cases (51 MDD and 52 controls) analyzed retrospectively.For the experimental group of MDD patients, the Hamilton Depression Rating Scale (HAM-D) and Hamilton Anxiety Rating Scale (HAM-A) were applied and compared to platelet parameters.Results: The study found that platelet large cell ratio (PLC-R) and platelet large cell count (PLC-C) values were higher in the MDD group compared to healthy controls.ROC analysis showed that PLC-C > 91.24 had 70.6% sensitivity and 80.8% specificity for MDD.Pearson correlation analysis showed that PLC-C values and HAM-D scores correlated positively in MDD patients.Conclusions: A simple CBC analysis detects PLC-R and PLC-C.Indices that give the ratio (PLC-R) and number (PLC-C) of larger and more active platelets in terms of coagulation should be emphasized.Our study found higher PLC-R and PLC-C values in patients with MDD compared to the control group, and increased PLC-C values with increased severity of depression.Thus PLC-C may be a useful marker for the increased coagulation activity observed in MDD patients and MDD patients with high PLC-R and PLC-C values may benefit from preventive antithrombotic therapy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".