Gap Arthroplasty versus Interpositional Arthroplasty for Temporomandibular Joint Ankylosis: A Meta-Analysis
Bibliographic record
Abstract
Gap arthroplasty (GA) and interpositional arthroplasty (IA) are widely used for the treatment of temporomandibular joint ankylosis (TMJA). However, controversy remains as to whether IA is superior to GA. PubMed, EMBASE, the Cochrane Library, the Web of science and the China National Knowledge Infrastructure were searched for literature regarding these procedures (published from 1946 to July 28, 2014). A study was included in this analysis if it was: (1) a randomized controlled trial or non-randomized observational cohort study; (2) comparing the clinical outcomes between GA and IA with respect to the maximal incisal opening (MIO) and reankylosis; (3) with a follow-up period of at least 12 months. The methodological quality of the included studies was evaluated according to the Newcastle-Ottawa Scale Eight non-randomized observational cohort studies with 272 patients were included. All the statistical analyses were performed using the RevMan 5.3 and Stat 12. The pooled analysis showed no significant difference in the incidence of reankylosis between the IA group (13/120) and the GA group (29/163) (RR= 0.67, 95% CI=0.38 to 1.16; Z=1.43, p=0.15). The IA group showed a significantly larger MIO than the GA group (MD=1.96, 95% CI=0.21 to 3.72, Z=2.19, p=0.03, I(2)=0%). In conclusion, patients with TMJA could benefit more from IA than GA, with a larger MIO and a similar incidence of reankylosis. IA shows to be an adequate option in the treatment of TMJA based on the results of maximal incisal opening.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.058 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".