Digital Infrared Thermographic Imaging for Limb Salvage in Patients at Risk of Amputation: Prospective Observational Study
Bibliographic record
Abstract
Background: Scores and prediction models such as the mangled extremity severity score (MESS) for trauma and the Wound, Ischemia, and foot Infection (WIfI) classification for diabetic foot ulcers help in the decision-making process for amputation. However, these tools can be subjective as they depend on the experience of the medical staff applying them. Objective: This study aimed to assess the impact of temperature measurement using infrared thermal imaging in extremity salvage in patients at risk of limb amputation. Methods: We included 29 patients who sought a second opinion after an amputation recommendation. Infrared thermographic images were acquired to measure the temperature difference (ΔT) between the injured and uninjured limbs. For patients with salvaged limbs, we provided clinical follow-up for up to 12 weeks. Results: Of the 29 patients enrolled in the study, 27 limbs were salvaged. Thermographic imaging allowed the distinction into two groups: the first group of 18 patients with mean ΔT value of -3.6 °C (SD 1.99 °C) , and the second group of 9 patients with mean ΔT of 3.36 °C (SD 2.71 °C). None of the patients in either group showed progression in ΔT values within the first 5 days; at the twelfth week, ΔT values approached 0 °C at wound closure. Of the two patients who required amputation, one showed an initial ΔT of -4.3 °C, which worsened to -5 °C by the fifth day, and the other patient showed an initial ΔT of -4.5 °C, which worsened to -5.8 °C by the fifth day. Conclusions: Digital infrared thermography is a tool that may help guide limb salvage in patients with uncertain clinical diagnoses. This imaging modality allows visualization of thermal differences and patterns derived from thermal changes in patients at risk of limb amputation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".