Orthopedic hardware in trauma – A guided tour for the radiologist-Associated complications (Part 2)
Bibliographic record
Abstract
With the increasing number of options available for surgical management of fractures now available, it is imperative that radiologists should familiarize themselves with the various hardwares used to provide a good support system for orthopedic surgeons. Understanding fracture union and “why a device may fail” are basic concepts that have been discussed in this review article, as their success is mutually dependent. While it may be easy to identify frank loosening, fracture, or migration of the hardware, it is more important to identify any early signs of these complications. However, before that, as a radiologist, one should be able to accurately identify the hardware type, assess their position, and then identify any potential complications. Another important aspect that is clinically important is the ability to differentiate between aseptic and septic loosening. Apart from these, avascular necrosis, pseudoaneurysms, bursitis, muscle impingement with atrophy, adverse reaction to metal debris, nerve impingements, traumatic neuroma formation, tendon impingement, snapping syndromes, and sarcoma are uncommon complications that may be rarely encountered. While conventional radiology is still the backbone of radiological evaluation, CT, MRI, and Ultrasound can be used as problem-solving tools, further aiding in the diagnosis of any hardware-related complications. In this series, we have also described a checklist based approach of reporting so that the radiologist can accurately identify the hardware, assess their position, and identify any potential complications. We hope that this learning will facilitate the interobserver consensus and standardization of reports.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".