Beyond the bones and joints: a review of ligamentous injuries of the foot and ankle on 99mTc-MDP-SPECT/CT
Bibliographic record
Abstract
99mTc-MDP-single photon emission computed tomography (SPECT)/CT has established itself as a useful modality in the assessment of the painful foot and ankle, owing to its ability to depict anatomical and functional information simultaneously. Despite its growing popularity, much of the literature describing the utility of 99mTc-MDP-SPECT/CT of the foot and ankle is limited to osseous and articular pathology, with descriptions of ligamentous pathology limited to just a handful of cases. Though assessment of soft tissues using a combination of bone tracer and CT is limited, with a sound understanding of the regional anatomy, it is certainly within the scope of 99mTc-MDP-SPECT/CT to accurately identify ligamentous injuries based on focal tracer uptake patterns and suspected ligamentous injuries should be reported as such. This article provides a case-based pictorial review of the 99mTc-MDP-SPECT/CT appearances of the various ligamentous injuries of the foot and ankle with case examples and MRI correlation where available, most of which have yet to be described. The typical patterns of bone tracer uptake and associated CT abnormalities that may be observed are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".