Temporomandibular joint total replacement using the Zimmer Biomet Microfixation patient-matched prosthesis results in reduced pain and improved function
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate pain and maximum interincisal opening (MIO) in patients treated with the Zimmer Biomet Microfixation patient-matched alloplastic temporomandibular joint (TMJ) prosthesis. STUDY DESIGN: We performed a retrospective cohort study of patients who had undergone bilateral or unilateral TMJ total joint replacement (TJR). The primary outcome variables were pain and MIO, which were measured at various time points between 12 and greater than 60 months. Secondary outcomes included perceived masticatory efficiency and patient satisfaction. RESULTS: A total of 33 patients (62 joints) met the inclusion criteria for the study. The relationship between time and the change in pain scores, although significant immediately after surgery in an unadjusted model, was not statistically significant in an adjusted model. A statistically significant improvement between time and MIO was noted in both adjusted and unadjusted models. The majority of patients (91%) reported subjective improvement in their diet. Similarly, 91% of patients felt that TJR was beneficial and, in retrospect, would repeat their decision to undergo TJR. CONCLUSIONS: Patients treated with the Zimmer Biomet Microfixation patient-matched TMJ prosthesis experience improvements in pain, MIO, and ability to masticate. Future studies are needed to assess long-term outcomes prospectively.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".