Teeth in a Day® for the Maxilla and Mandible: Case Report
Bibliographic record
Abstract
BACKGROUND: A growing body of evidence indicates that successful osseointegration of dental implants can take place in the wake of immediate loading, providing that bone quality and quantity are adequate, and patients follow postsurgical instructions carefully. PURPOSE: The goal of this report is to demonstrate the efficient treatment protocol based on immediate loading for both the maxilla and mandible, including extraction site locations. MATERIALS AND METHODS: Following extraction of the remaining anterior mandibular teeth, 18 Brånemark implants (Nobel Biocare AB, Gothenburg, Sweden), including two zygoma and two pterygoid implants, were installed in both arches in accordance with the Teeth in a DayTM protocol developed by the authors 9 years ago. This protocol uses an acrylic screw-retained prosthesis, with steel prosthetic copings embedded, supported by full-size Brånemark implants to prevent micromotion at the bone-to-implant interface. RESULTS: Only 1 of the 18 immediately loaded implants failed to osseointegrate. Three years after completion of treatment, the patient reported functioning well with no complications. CONCLUSIONS: When appropriate subjects are selected, the Teeth in a Day protocol offers patients a number of significant advantages, including condensed treatment time, reduced postsurgical discomfort, and almost instantaneous improvement in speech and masticatory function, esthetics, and patient self-image.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".