Indicators Differentiating Thrombotic Thrombocytopenic Purpura From Other Thrombotic Microangiopathies in a Canadian Apheresis Referral Center
Bibliographic record
Abstract
OBJECTIVES: Thrombotic thrombocytopenic purpura (TTP) is a rare thrombotic microangiopathy (TMA) caused by ADAMTS13 deficiency with mortality of up to 90% in the absence of treatment, typically therapeutic plasma exchange (TPE). TTP presents similarly to other TMAs in which TPE is ineffective and associated with morbidity and additional costs. Thus, we sought to assess clinical and laboratory parameters differentiating TTP from other TMAs in our institution's catchment population. METHODS: We reviewed 8 years of data from a Canadian provincial apheresis center, including 100 patients with suspected TMA who underwent ADAMTS13 testing, 35 of whom were diagnosed with TTP. We assessed clinical and laboratory parameters to identify discriminators of TTP and assigned PLASMIC TTP prediction scores. RESULTS: We observed a higher frequency of neurologic symptoms, more severe thrombocytopenia, and less creatinine elevation in TTP relative to other TMAs. High PLASMIC scores (6-7 points) had 83% sensitivity and 88% specificity for TTP diagnoses; however, ADAMTS13 activity testing was required for correct diagnoses in 14 cases. CONCLUSIONS: Clinical and laboratory parameters including PLASMIC scoring may lead to misdiagnosis in some cases of TMA. ADAMST13 activity testing provides definitive diagnosis of TTP, supporting the role of rapid turnaround ADAMTS13 testing for appropriate treatment of TMAs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".