Cost‐effectiveness analysis of different imaging strategies for diagnosis of haemophilic arthropathy
Bibliographic record
Abstract
SUMMARY: Physiotherapy and radiography of the joints are standard diagnostic strategies for assessment of haemophilic arthropathy. The use of ultrasonography as an adjunct tool for early diagnosis of haemophilic arthropathy may optimize factor replacement therapy. The objective of this study was to compare costs and effectiveness of physiotherapy, radiography and ultrasonography (intervention strategy, IS) with physiotherapy and radiography alone (standard care strategy, SCS) for diagnosing soft tissue and osteocartilaginous changes in haemophilic joints. We retrospectively compared costs and effectiveness of IS vs. SCS in knees, ankles and elbows of 31 children (age range, 4-17 years) with haemophilia A (n = 30) or B (n = 1) (IS, n = 11; SS, N = 20). Direct health care costs were measured from the provincial health care perspective. Effectiveness was measured by false-negative (FN) rates in each study arm by comparing presence or absence of abnormalities of physiotherapy and imaging exams to the reference standard measure (MRI). In scenario 1, all diagnostic tests matched with MRI. In scenario 2, at least one diagnostic test matched with MRI. The IS was more costly [incremental cost/100 patients, Canadian (CND) $4987] and more effective (incremental effectiveness, FNs/100 patients for scenario 1, -4.09, and for scenario 2, -41) for both scenarios. The incremental cost-effectiveness ratios for scenario 1 and for scenario 2 were CND$1166 and CDN$116 per FN result averted per 100 patients, respectively. In conclusion, in the short-term, the incorporation of ultrasonography in a test set for diagnosis of haemophilic arthropathy substantially improved the diagnostic performance of this test set, however at an increased cost.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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) | 0.003 | 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".