Current Use of Infrared Thermography in Orthopaedic and Bone or Joint Trauma Patients–Can We Identify Postoperative Infection? A Narrative Systematic Review
Bibliographic record
Abstract
Aim and background: Technological advances have made infrared thermography (IRT) sensitive, noncontact, and low cost for medical applications and it is used in a range of fields. A widening body of research has investigated IRT in the orthopaedic setting, including the investigation of orthopaedic infection. Infrared thermography could provide a rapid, low-cost, objective, noncontact technique to aid in the diagnosis of orthopaedic infections. Methods: Electronic searches of MEDLINE, CINAHL, and EMBASE from 2000 to 2024 were made. The search strategy aimed to include all studies in adults investigating the use of IRT in orthopaedic and bone or joint trauma patients and those studies which provide baseline values, including in patients with infection. Articles were screened by title and abstract by two authors. Bias was assessed using the Newcastle-Ottawa Scale tool. Studies were heterogeneous; therefore, results were summarised in tables and presented as a narrative synthesis. Results: The search identified 36 studies. Studies have shown that IRT is useful in fracture or soft tissue diagnosis, detecting periprosthetic infection, and it may have a role in screening healthy subjects. There is still considerable variance in the application of IRT in the trauma and orthopaedic setting. Conclusion: Infrared thermography is sensitive to skin temperature changes in infected limbs following orthopaedic surgery and may be used as a low-cost, noncontact, irradiation-free screening tool to identify orthopaedic infection in the future. Future studies should identify the cost effectiveness of IRT in clinical practice. Barriers include the low incidence of orthopaedic infection and large number of confounders that can affect IRT readings. Clinical significance: Infrared thermography can provide rapid information that may be a useful adjunct in the emergency department or outpatient clinics to diagnose a range of orthopaedic conditions, including infection. Current research has yet to demonstrate clinical significance. How to cite this article: . Current Use of Infrared Thermography in Orthopaedic and Bone or Joint Trauma Patients-Can We Identify Postoperative Infection? A Narrative Systematic Review. Strategies Trauma Limb Reconstr 2024;19(3):141-148.
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".