A Systematic Review of Observational Studies Evaluating Implant Placement in the Maxillary Jaws of Medically Compromised Patients
Bibliographic record
Abstract
BACKGROUND: Even though the efficacy of implant treatment and the excellent success rates that modern implant surfaces yield remain unchallenged, there is limited information available on implant success rates in medically compromised patients. PURPOSE: The aim of this systematic review was to evaluate the survival of implants placed in the maxillary jaws of medically compromised patients. MATERIALS AND METHODS: Two reviewers using predefined selection criteria performed an electronic search complemented by a manual search, independently and in duplicate. RESULTS: After the final selection, 11 studies reporting on four distinct medical conditions were included out of 405 potentially eligible titles. In detail, three studies reported on implants placed in diabetic patients, six on implants placed in patients with a history of oral cancer, one on implants in patients with a history of epilepsy, and one on implants in patients with autoimmune rheumatoid arthritis. CONCLUSIONS: Placement of maxillary implants in medically compromised patients seems to yield acceptable survival rates. Implant survival in well-controlled diabetic patients, patients diagnosed with rheumatoid arthritis, and patients treated for severe epilepsy is comparable to that in healthy patients. Implants placed in the maxillae of patients treated for oral cancer may attain osseointegration less predictably than in the mandible.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".