Orthodontic bonding to porcelain: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To use a systematic review to determine which materials and technique/protocol present the highest success rate in bonding brackets to porcelain surfaces. MATERIALS AND METHODS: Different databases were searched without limitations up to July 2013. Additionally, the bibliographies of the finally selected articles were hand searched to identify any relevant publications that were not identified earlier. In vitro and in vivo articles were included. RESULTS: No in vivo articles were found that fulfilled the inclusion criteria. A total of 45 in vitro articles met all inclusion criteria. They were published between 2000 to July 2013. CONCLUSIONS: The best protocol described in this review is the etching of 9.6% hydrofluoric acid for 1 minute, rinsed for 30 seconds, and then air-dried. The etching of hydrofluoric acid should be followed by an application of silane. Considering the harmful effects of etching with hydrofluoric acid, another appropriate suggestion is mechanical roughening with sandblasting followed by an application of silane.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".