Spectral Analysis of Platelet Oscillations in Cyclic Thrombocytopenia.
Bibliographic record
Abstract
Abstract Abstract 4459 Introduction Cyclic thrombocytopenia (CT) is a rare autoimmune condition characterized by predictable oscillations in platelet count levels. Periodic platelet cycles in CT have not been well characterized. Methods We studied three patients with wide platelet count fluctuations to identify patterns consistent with CT. Medical charts were reviewed and platelet levels and treatments were abstracted. Platelet counts were plotted over time for each patient. Spectral analyses were completed using the method of Lomb and Scargle to determine if platelet count fluctuations were random or had a stable period of oscillation. Where stable periods were identified (fixed time from peak to peak) the likelihood of those periods being random was calculated using the false-alarm probability. Results Regular periods of platelet oscillation were identified for each patient (Table 1). Periods varied between 23 and 42 days and were not random. Patient I had a 2-year remission induced by immune suppressive medications during which time the platelet count was normal and stable. Platelet count oscillation amplitudes were 350 ×109/L both at presentation of CT and at relapse. Gradual building and decaying of oscillations were noted at the onset of relapse, and remission, respectively. Patient II Matching a sine curve to the platelet data demonstrated that platelet nadirs of <20 ×109/L could be predicted to within 4 days for every cycle up to one year in advance. Patient III had typical CT oscillations for 6 years, but thereafter platelet fluctuations became random and no regular period was observed. Treatment with eltrombopag, a thrombopoietin (TPO) mimetic agent for 5 days resulted in a peak platelet count peak in excess of 1200 ×109/L in this patient. Conclusion In three patients with CT, we identified regular periodic oscillations in platelet counts that were non-random. Some patients have predictable cycles that may allow timed delivery of treatments such as TPO-mimetic agents. These findings may provide insight into the nature of the autoantibody in CT. Disclosures: Arnold: Hoffman-LaRoche: Research Funding; Amgen: Consultancy, Honoraria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".