Bibliographic record
Abstract
Resume Touchant enfants comme adultes avec des evolutions et des implications differentes, les traumatismes dentaires et/ou alveolaires sont des lesions frequentes motivant la consultation d’un praticien, souvent en urgence, puis la collaboration entre stomatologues ou chirurgiens maxillofaciaux et dentistes. La difficulte reside en la necessite d’un diagnostic et d’une prise en charge precoces (parfois impossibles a obtenir en cas de polytraumatisme ou eloignement d’un specialiste), mais est egalement liee a la nature du terrain : enfants, detresse vitale associee, etat buccodentaire mediocre… Quoi qu’il en soit, l’evolution de ces lesions est imprevisible et une grande retenue devra etre faite, meme en cas de succes apparent, aupres du patient ou de sa famille. Compte tenu des implications fonctionnelles, cosmetiques et financieres en cas d’echec du traitement, la redaction rigoureuse du certificat medical initial est d’une importance capitale. Une surveillance attentive est indispensable : hebdomadaire en debut de traitement, elle s’etendra ensuite sur plusieurs annees.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.962 | 0.961 |
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".