SPECT-CT TO EVALUATE PAINFUL TOTAL ANKLE REPLACEMENT
Bibliographic record
Abstract
Introduction Single photon emission computed tomography (SPECT) can be used to create a three dimensional image of a radiopharmaceutical bone scan. This combined with high resolution CT scan (SPECT-CT) with bone windows allows the linking of the information obtained in both investigations. The multiplanar anatomical information provided by CT is therefore linked with the functional, biological information of bone scintigraphy . The painful total ankle replacement has a number of potential causes of discomfort including impingement and loose components. Correct identification of the source of pain will assist surgeons in treating the source of the pain while avoiding unnecessary surgery. We present our experience of the use of SPECT-CT to investigate patients with ongoing pain following Total Ankle Replacement (TAR). Materials and Methods A retrospective analysis of all patients having SPECT-CT for continuing pain following TAR. Scans were requested in addition to plain radiographs, joint aspiration and blood testing. Results: A total of 12 patients were identified. The scan proved helpful in all cases. 5 patients showed increased uptake around one or both prostheses signifying loosening which was not apparent on plain films. Gutter impingement was identified in 4 patients. One patient had a talo-navicular non-union, one patient demonstrated sub-talar joint arthrosis and one patient showed no bony abnormality but soft tissue impingement at arthroscopy. Discussion SPECT-CT provides a useful adjunctive investigation in the work-up of the patient with ongoing pain in a TAR, particularly in the cases of component loosening where plain x-rays may be limited. The SPECT-CT assists in the correct anatomical localization of the pain and has assisted in identifying the correct surgical treatment. Disadvantages include cost and availability of scanners.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".