Self-reported tooth and implant prognosis evaluation based on radiographic bone loss: a cross sectional study
Bibliographic record
Abstract
Abstract Introduction: Tooth prognosis evaluation involves continual assessments to guide patient-centered treatment plans. This means that the tooth prognosis may dictate whether a tooth is restored, extracted, or maintained. Aim of study: The aim of this work was to evaluate current trends in tooth prognosis evaluation based on radiographic bone loss amongst dental practitioners. Material and Methods: A survey including demographic questions and ten radiographs (vertical bitewings or peri-apical) showing bone loss around teeth and implants were distributed to dental practitioners. Practitioners were asked to determine the prognosis of the tooth or implant and suggest a percentage describing the likelihood of the tooth or implant surviving for ten years. Results: One of the ten radiographs provided for assessment was given good to fair prognosis by 100% of the participants. Only three out of the ten radiographs presented had strong suggestions for tooth retention. Recommendation for extraction by dental practitioners varied from 1-66% across the radiographs. Furthermore, practitioners predicted a 0% chance of ten-year survival for many of the teeth. Conclusions: Assessing prognosis based on radiographs only, is insufficient and clinical data provides invaluable information to establishing tooth prognosis. Dental professionals should understand that compromised teeth can outlive dental implants and our role as dental professionals is to prevent and treat oral diseases to preserve the dentition as long as possible.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".