Immediate Loading in the Maxillary Arch: Evidence-Based Guidelines to Improve Success Rates: A Review
Bibliographic record
Abstract
The reliability of immediately loaded dental implants in the mandible has prompted many to investigate their application in the maxilla. Although the body of literature is growing, the long-term survivability of immediate loading in the maxilla is still pending. This review of literature investigates the status of immediate loading of dental implants in the maxilla to determine its predictability as a treatment option for partial and complete maxillary edentulism. Current terminology in the field is summarized first. Subsequently, the rationale and advantages of immediate loading in the maxilla are reviewed, and the relationships between immediate loading and osseointegration, primary stability, implant design, micromotion, immediate implant placement, and bone character are explored. The importance of a prosthodontically driven implant treatment plan emphasizing the role of splinting a high-precision and passively fitting implant restoration with reduced micromotion under function is summarized. The reliability and predictability of immediately loaded implants as a treatment option are proposed, and recommended guidelines for the successful delivery of immediately loaded implants in the maxilla are presented.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".