Platelet serotonin levels support depression scores for women with postpartum depression
Bibliographic record
Abstract
OBJECTIVE: It is very challenging to make an unbiased diagnosis of psychiatric illness. Platelets have long been proposed as easily obtainable, neurological models of serotonergic neurons. This study examined whether a new measurement for platelet serotonin could aid in the diagnosis of postpartum depression and support the results from questionnaires. METHODS: Study participants included 11 patients with postpartum clinical depression according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, criteria. Blood was donated either at acute onset of depression before treatment (n = 5) or while patients were nonresponsive to paroxetine treatment (n = 8; 2 of these patients dropped out). A follow-up sample was donated approximately 8 weeks later during paroxetine treatment (n = 11). Platelet serotonin was determined with a new immunocytochemical assay and standard high-pressure liquid chromatography. Serotonin levels were compared with Hamilton Depression Rating Scale scores. RESULTS: Platelet serotonin levels in patients with depression before paroxetine treatment or nonresponsive to their initial paroxetine regimen were reduced to 50% of normal levels. Treatment-induced severe reduction of platelet-associated serotonin only occurred in responsive patients. Mean platelet serotonin levels were significantly lower in responders (17.3%, standard deviation [SD] 4%), compared with nonresponders (33.4%, SD 8%; p < 0.001). CONCLUSION: Platelet serotonin levels obtained with a new immunocytochemical test correlated well with results from depression scoring and might be useful as evidence-based support for questionnaires.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".