Bibliographic record
Abstract
Most pediatric dentists provide clinical and after-hours care for dental injuries in their private offices, clinics and hospitals. They are trained and capable to perform the surgery, fracture reduction, splinting and single root endodontic treatment that are part and parcel of dental trauma management. However, for many clinicians, specialists included, dental trauma cases present infrequently and unexpectedly, with diagnosis and management continually evolving with advances in scientific information. Treatment that was current the last time they treated an injury may be out of date the next time clinicians are presented with similar cases. Trauma research is scattered among specialty-specific journals that focus on pediatric dentistry, endodontics, oral and maxillofacial surgery, sports medicine and even periodontics. One trauma-specific journal, Endodontics and Dental Traumatology was renamed Dental Traumatology in January 2001 to reflect the multidisciplinary nature of dental trauma research and to encourage contributions from a broader range of specialties. This update is based on a number of developments in traumatology over the past five to seven years but is focused on trends. Unfortunately, trauma research is concentrated in only a few centers and they are mainly outside the United States. Editorial calls to action for dental trauma research seem to be hampered by overworked front-line clinicians or scarce resources.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".