Diagnosis of Raynaud’s Phenomenon by <sup>99m</sup>Tc-Hydroxymethylene Diphosphonate Digital Blood Flow Scintigraphy After One-hand Chilling
Bibliographic record
Abstract
OBJECTIVE: We introduce the use of (99m)Tc-hydroxymethylene diphosphonate (HDP) digital blood flow scintigraphy to diagnose Raynaud's phenomenon (RP). METHODS: Fifty-seven patients with RP and 60 healthy controls were recruited. One hand was chilled by immersion into water at 4 degrees C, and then an intravenous bolus of 740 MBq of (99m)Tc-HDP was injected. The radioactivity from the second to the fifth fingers of both hands was recorded. Acquisition was performed at a rate of one frame per 2 seconds until 155 frames. We calculated 4 ratios by comparing the activity curves of the chilled hand with those of the ambient hand. RESULTS: The chilled to ambient hand ratio of the initial slope was significantly lower in the patients with RP (0.28 +/- 0.18) than in the controls (0.78 +/- 0.20) (p < 0.001). The chilled to ambient hand ratio of the first peak height, 30-second area under the curve, and blood pool uptake were also lower in the patients with RP than in controls (p < 0.001 for each). The initial slope ratio of 0.51, used as a cutoff value, showed a sensitivity of 91.2% and specificity of 93.3%. The loss of the initial spike curve, the presence of a slowly progressing radioactivity curve, and the inhomogeneous radioactivity uptake in the blood pool image in either hand were characteristic findings of the patients with RP (p < 0.001). CONCLUSION: (99m)Tc-HDP digital blood flow scintigraphy after one-hand chilling is a noninvasive, accurate, and quantitative method to evaluate RP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".