306. A MULTICENTRE RELIABILITY AND VALIDITY STUDY OF LASER SPECKLE CONTRAST IMAGING AND THERMOGRAPHY IN PATIENTS WITH RAYNAUD’S PHENOMENON SECONDARY TO SYSTEMIC SCLEROSIS
Bibliographic record
Abstract
Background: Objective and reliable outcome measures for clinical trials of novel drugs to treat systemic sclerosis (SSc) related Raynaud’s phenomenon (RP) are currently lacking. Laser speckle contrast imaging (LSCI) and thermography are two non-invasive measures of perfusion that show excellent potential but require further clinical assessment. The purpose of this multi-centre study was to determine the reliability and validity of a mild cold challenge protocol using both LSCI and thermography (including low-cost mobile phone thermography). Methods: 159 patients with RP secondary to SSc were recruited from 6 UK specialist SSc centres. Patients underwent a cold challenge on 2 consecutive days; 15°C water submersion of gloved hands for 1 minute, then un-gloved reperfusion and rewarming at 23°C room temperature over 15 minutes. Baseline and changes in blood flow and temperature were imaged simultaneously using LSCI (relative perfusion) and thermography (skin temperature), respectively. Mobile phone thermography images were taken at baseline, 0 and 15 minutes post cold challenge. Parameters (Table 1) were calculated locally and data analysis performed centrally. Data were averaged across 8 digits to obtain a single measurement for each parameter for each technique at both visits. Test–retest reliability was assessed using intra-class correlation coefficients (ICC). Estimated latent correlations assessed the convergent validity of the LSCI and thermography (R version 3.2.3).
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".