Handicap et accès aux soins dentaires : étude rétrospective des patients de l’Hôpital Saint André à Bordeaux
Bibliographic record
Abstract
In France, the disabled population amounts to 9.6 million people divided into 6 categories of disabilities: Mental, Sensory, Motor, Psychic, Autism Spectrum Disorder and pervasive developmental disorder and Dys Disorders. The care in the dental office is complicated and the general anesthesia (GA) persists as the last therapeutic alternative. The objective of this study is to describe the discrepancy between the number and the nature of the care planned and carried out during the first consultation either in the dental office or under GA. A retrospective study of patients with disabilities at the CHU Saint André in Bordeaux between 2019 and 2021 was performed. Patients for GA mainly came as a result of pain while in ambulatory they came for follow-up. More than a quarter of the patients had waited more than a year for their GA surgery. The number of procedures performed was almost double the number initially planned and was mainly caries care and extractions. In the outpatient setting, the procedures were mainly prophylactic. After the study, almost two-thirds of the patients were lost to follow-up. A total of 64 patients had to be redirected to ensure better management. It is therefore important to increase awareness of the importance of dental follow-up, but also to facilitate access to dental operating rooms and to train better students in the various behavioral approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".