A novel digital technique for screw-access retrieval system fabrication: A technique paper
Bibliographic record
Abstract
Retrieving implant-supported restorations can be very challenging due to variations in implant systems, implant angulation, and the location of screw access. This procedure can introduce potential complications during the retrieval process, such as damaging the screw head or the internal threads of the implant. The utilization of a retrieval guide for accurate positioning of a cement-retained implant crown. In this technique, intra-oral scans, along with cone-beam radiographs, were utilized for precise identification of the screw access position. In this clinical report, the successful retrieval of a cement-retained implant crown was described using a novel digital retrieval system. This clinical report presents a proof-of-concept technique for retrieving implant-supported crowns. This technique paper presents a potentially predictable innovative approach to identifying the screw access position in old cement-retained implant-supported crowns. Successful retrieval of a cement-retained crown was completed safely. The use of this guide could potentially eliminate the risk of damaging the head of the screw, which can eventually lead to impossible retrieval.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".