Evolving Horizons in Temporomandibular Joint Total Replacement: A Comprehensive Narrative Review
Bibliographic record
Abstract
Total temporomandibular joint (TMJ) replacement has become a vital solution for managing end-stage TMJ disorders and significantly enhancing mandibular function and patient quality of life. This narrative review explores the historical developments, clinical objectives, biomaterials, devices, surgical techniques, preoperative preparation, complications, management approaches, and recent innovations in TMJ total joint replacement (TJR). The review draws from a literature search conducted across PubMed, Scopus, Web of Science, and Google Scholar from database inception to August 10, 2025, using terms like "total temporomandibular joint replacement," "TMJ TJR," and "alloplastic TMJ prosthesis," focusing on English-language peer-reviewed articles, reviews, and clinical studies. Current practice leans toward alloplastic reconstruction, utilizing titanium alloys for osseointegration and ultra-high molecular weight polyethylene (UHMWPE) for low-friction surfaces, supported by custom designs via computer-aided design and manufacturing (CAD/CAM). Surgical approaches, including preauricular, retromandibular, and rhytidectomy techniques, have been refined to protect critical structures, such as the facial nerve and internal maxillary artery. Preoperative preparation involves patient education on risks, such as infection and nerve injury; advanced imaging with computed tomography (CT) and 3D reconstructions; and strict aseptic measures. Complications, such as heterotopic bone formation and dislocations, are managed with excision, fat grafting, or surgical redesign, while nerve injuries and synovial issues require targeted interventions. Recent advances have highlighted bioengineered solutions, including bioresorbable composites and nanomaterials, that are promising for merging alloplastic and biological benefits. Currently, these developments, underpinned by ongoing research, position TMJ TJR as a cornerstone of maxillofacial surgery, with the potential to reduce donor morbidity and improve long-term outcomes, although challenges such as infection rates and material hypersensitivity persist, necessitating continued refinement.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".