In the dental implant era, why do we still bother saving teeth?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Teeth are vital sensory organs that contribute to our daily activities of living. Unfortunately, teeth can be lost for several reasons including trauma, caries, and periodontal disease. Although dental trauma injuries and caries are more frequently encountered in a younger population, tooth loss because of periodontal disease occurs in the older population. In the dental implant era, the trend sometimes seems to be to extract compromised teeth and replace them with dental implants. However, the long-term prognosis of teeth might not be comparable with the prognosis of dental implants. Complications, failures, and diseases such as peri-implantitis are not uncommon, and, despite popular belief, implants are not 99% successful. Other treatment options that aim to save compromised or diseased teeth such as endodontic treatment, periodontal treatment, intentional replantation, and autotransplantation should be considered on an individual basis. These treatments have competing success rates to dental implants but, more importantly, retain the natural tooth in the dentition for a longer period of time. These options are important to discuss in detail during treatment planning with patients in order to clarify any misconceptions about teeth and dental implants. In the event a tooth does have to be extracted, procedures such as decoronation and orthodontic extrusion might be useful to preserve hard and soft tissues for future dental implant placement. Regardless of the treatment modality, it is critical that strict maintenance and follow-up protocols are implemented and that treatment planning is ethically responsible and evidence based.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.012 |
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 it