Common Fractures and Dislocations of the Hand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
LEARNING OBJECTIVES: After reading this article, the participant should be able to: 1. Describe the concept of early protected movement with Kirschner-wired finger fractures to the hand therapist. 2. Choose the most appropriate method of fracture fixation to achieve the goal of a full range of motion. 3. Describe the methods of treatment available for the most common fractures and dislocations of the hand. BACKGROUND: The main goal of treatment of hand and finger fractures and dislocations is to attain a full range of wrist and nonscissoring finger motion after the treatment is accomplished. This CME article consists of literature review, illustrations, movies, and an online CME examination to bring the participant recent available information on the topic. METHODS: The authors reviewed literature regarding the most current treatment strategies for common hand and finger fractures and dislocations. Films were created to illustrate operative and rehabilitation methods used to treat these problems. A series of multiple-choice questions, answers, discussions, and references were written and are provided online so that the participant can receive the full benefit of this review. RESULTS: Many treatment options are available, from buddy and Coban taping to closed reduction with immobilization; percutaneous pins or screws; and open reduction with pins, screws, or plates. Knowledge of all available options is important because all can be used to achieve the goal of treatment in the shortest time possible. The commonly used methods of treatment are reviewed and illustrated. CONCLUSIONS: Management of common hand and finger fractures and dislocations includes the need to focus on achieving a full range of motion after treatment. A balance of fracture reduction with minimal dissection and early protected movement will achieve the goal.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it