Bibliographic record
Abstract
A large number of classification systems exist to assist in the evaluation and treatment of periprosthetic fractures following joint replacement. They vary in the language or categorisation they employ, the joints to which they are differently applied, the factors they assess, and the hierarchy or importance assigned to those factors. Not all incorporate the three most important variables which should govern treatment (fracture location, implant fixation, bone quality), nor the factors which have been demonstrated to most prominently influence outcomes. To a greater or lesser extent they attempt to include the principles of the Vancouver Classification System, and yet they differ in ways that lead to awkwardness in their application within the clinical setting. As an example, for fractures of the patella alone, three different systems have been proposed. As the result of an international effort endorsed by the AO/OTA, the Universal Classification System, or UCS, has been developed. It incorporates the most important factors that should influence evaluation, treatment, and outcomes evaluation. It applies the system to the musculoskeletal system as a whole, regardless of the joint involved or the bone that is broken. And it uses a single common language to describe the injury and prescribe the logical principles of treatment. It is hoped the UCS will appeal to our colleagues worldwide and will assist all of us in the care of our patients who suffer a periprosthetic fracture after joint replacement; be that the hip, knee, ankle, shoulder, elbow or wrist. The UCS can be applied with equal ease and merit to all.
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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".