The Role of FLIR ONE Thermography in Complex Regional Pain Syndrome
Bibliographic record
Abstract
ABSTRACT: Complex regional pain syndrome remains a debated syndrome characterized by symptoms and signs, including pain, sensory disturbances, thermal asymmetry, edema, and motor impairments. Thermography is a tool that assesses skin surface temperature distribution. Current literature focuses on the role of thermography for diagnostic purposes; however, its role in monitoring the response to treatment in complex regional pain syndrome is unclear. We present a case series of four patients with complex regional pain syndrome where a FLIR ONE thermal imaging camera (FLIR Systems, Inc, Wilsonville, OR) was used to assist in the diagnosis of complex regional pain syndrome, capture the effects of diagnostic nerve blocks to evaluate a peripheral nerve contribution to complex regional pain syndrome, as well as monitor and assess treatment efficacy with prednisone and surgery. Four patients were screened by clinical assessment to meet the Budapest Criteria. The thermal images revealed improvement in the temperature distribution after therapeutic intervention. We also noted temporary but immediate changes on thermal imaging with diagnostic nerve blocks. Our cases suggest that the FLIR ONE thermal imaging camera has the potential to be an accessible monitoring tool to assist in diagnosis and assess treatment efficacy in complex regional pain syndrome over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".