10.1016/j.quint.2012.04.007
Bibliographic record
Abstract
Desde el punto de vista epidemiologico mas de la mitad de los ninos y adolescentes sufren en algun momento un traumatismo dental que afecta a la dentadura temporal en el 30% de los casos y a la dentadura permanente en aproximadamente el 20-25% de los casos. El tratamiento de las lesiones generadas supone para el odontologo una tarea ingente y un gran reto. Ademas del tratamiento previsto, que dependera fundamentalmente de la fase de desarrollo de la denticion, reviste una importancia primordial el tratamiento inmediato. Hallar una solucion terapeutica competente para el tratamiento de los traumatismos dentales no solo exige conocimientos fundados en materia de restauracion, sino tambien conocimientos especificos de endodoncia, periodoncia y cirugia oral. Estos conocimientos son imprescindibles, dado que algunas lesiones en la denticion temporal aparentemente banales pueden requerir tratamientos a largo plazo hasta la edad adulta (como en el caso de trastornos de la mineralizacion, perdida de piezas, tratamientos de espacios edentulos con protesis o implantes). El hecho de que los traumatismos dentales no forman parte de la rutina diaria de la consulta dental y que el odontologo ha de reaccionar con inmediatez y tomar una decision diagnostica y terapeutica rapida y competente son factores que complican el tratamiento. En este trabajo se describen las complicaciones y las secuelas asociadas a traumatismos dentales y sirviendose de tres casos clinicos da consejos utiles para el tratamiento eficaz.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.845 | 0.723 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".