Implant vs screw loading protocols in orthodontics.
Bibliographic record
Abstract
OBJECTIVE: This systematic review presents the loading protocols applied when using implants and/or screws in orthodontic treatments. MATERIALS AND METHODS: Clinical trials which assessed the use of implants and/or screws for orthodontic anchorage and studies involving treatment on syndromic patients, surgery, other simultaneous treatments, or appliances (ie mini-plates) were considered. Electronic databases (Medline, Medline In-Process & Other Non-Indexed Citations, Lilacs, Pubmed, Embase, Web of Science, and All Evidence Based Medicine Reviews) were searched with the help of a senior Health Sciences librarian. Abstracts which appeared to fulfill the selection criteria were selected by consensus. The original articles were then retrieved and evaluated with a methodological checklist. References were also hand searched for possible missing articles. RESULTS: Eleven articles fulfilled the selection criteria established. Five studies involved the use of implants while six involved the use of screws for orthodontic purposes. An individual methodological analysis for each article was made. CONCLUSIONS: Loading protocols for implants involve a minimum waiting period of 2 months before applying orthodontic forces while loading protocols for screws involve immediate loading or a waiting period of 2 weeks to apply forces. Success rates for implants were on average higher than for screws.
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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".