Infrared Thermography—A Novel Tool for Monitoring Fracture Healing: A Critically Appraised Topic With Evidence-Based Recommendations for Clinical Practice
Bibliographic record
Abstract
CLINICAL SCENARIO: Stress fractures are one of the most common injuries in athletes. Unfortunately, they are hard to diagnose, require multiple radiology exams and follow-up which leads to more exposure to radiation and an increase in cost. Stress fractures that are mismanaged can lead to serious complications and poorer outcomes for the athlete. During the rehabilitation process, it would be beneficial to be able to monitor the healing of fractures to know when it is safe to gradually allow a patient to a return to sport because the return to activity is not usually objective and based on pain level. CLINICAL QUESTION: Can infrared thermography (IRT) be a useful tool to measure the pathophysiological state of the fracture healing? The aim of this critically appraised topic is to analyze the current evidence of IRT for measuring the temperature change in fractures to provide recommendations for medical practitioners. SUMMARY OF KEY FINDINGS: For this critically appraised topic, we examined 3 articles that compared medical imaging and IRT over multiple time points during the follow-up. The 3 articles concluded that a 1 °C asymmetry in temperature followed by a return to normal (less than 0.3 °C) temperature during the healing process of fractures can be monitored using IRT. CLINICAL BOTTOM LINE: Once the patient has been diagnosed with a fracture, IRT can safely be used to monitor the evolution of a fracture. When the thermogram progresses from a hot thermogram to a cold thermogram, the healing is considered good enough to return to sport. STRENGTH OF RECOMMENDATION: Grade 2 evidence exists to support IRT being used by clinicians to monitor fracture healing. Due to the limited research and novelty of the technology, the current recommendations are for following the treatment of the fracture once the initial diagnosis is made.
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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 | 0.020 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".