New Technologies in the Treatment of Base of Thumb Osteoarthritis
Bibliographic record
Abstract
Symptomatic osteoarthritis (OA) of the first carpometacarpal (CMC) joint is prevalent and debilitating, commonly affecting the elderly and postmenopausal population. This review highlights the latest advancements in the treatment of thumb CMC OA, which historically includes a range of nonsurgical and surgical options without a consensus benchmark. We will focus on innovative and emerging technologies. Nonsurgical treatments typically comprise custom braces and corticosteroid injections. In addition, this review explores advanced approaches such as 3D printed braces, which have improved patient satisfaction, and novel intra-articular injectables such as autologous fat, optimized by ultrasonography to enhance treatment precision and outcomes. Although standard surgical treatments include trapeziectomy, with or without ligament reconstruction and tendon interposition, more recent implant arthroplasty designs show promising long-term survival. Newer interventions include patient-specific instrumentation for metacarpal osteotomies, selective joint denervation, and innovative suspensionplasty devices, all marked by their increased precision and personalized care. However, it is important to note that these novel technologies are not yet established as superior to standard treatments of thumb CMC OA.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".