Post‐thrombotic syndrome in children: Measurement properties of CAPTSure, a new diagnostic tool
Bibliographic record
Abstract
BACKGROUND: CAPTsure (Clinical Assessment of PTS) is a new tool for diagnosis and severity rating of pediatric postthrombotic syndrome (PTS). Our objective was to test the reliability, measurement error, and minimal detectable change of CAPTSure. METHODS: Children aged newborn to 18 years who sustained upper extremity or lower extremity deep vein thrombosis (DVT) were enrolled ≥ 6 months after DVT diagnosis. Patients were assessed by 2 raters to determine the reliability of the clinician assessment component (CC) of CAPTSure. Patients/proxies completed CAPTSure at baseline and approximately 2 weeks later to assess test-retest reliability of the symptoms component (SC). RESULTS: Of 148 patients enrolled in the study; 30 had sustained either bilateral or both upper and lower extremity DVT. Hence, 178 extremities were assessed for PTS signs (86 upper extremity, 92 lower extremity). Intraclass correlation coefficient (ICC) for the CC was 0.89 (95% confidence interval [CI], 0.84-0.93) for upper extremity and 0.88 (95% CI, 0.83-0.92) for lower extremity. Nonclinicians performed 59% of measurements. Ninety-eight patients completed the SC at baseline and follow-up, for a total of 60 upper extremity and 61 lower extremity assessments. ICC for the SC was 0.89 (95% CI, 0.84-0.93) for upper extremity and 0.92 (0.87-0.95) for lower extremity. ICC for CAPTSure was 0.92 (95% CI, 0.87-0.95) for upper extremity and 0.93 (95% CI, 0.88-0.95) for lower extremity assessment. Measurement error ranged between 1.7 and 4.3 of 100 points. A change of approximately 11 of 100 points in CAPTSure score would be required to be confident that there was a change in PTS severity. CONCLUSION: CAPTSure has excellent reliability and a small measurement error, even when applied by nonhematologists.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".