Subjective Need for Implant Treatment among Middle‐aged People in Sweden and Denmark
Bibliographic record
Abstract
BACKGROUND: The use of oral implants in prosthodontics has become widespread and regarded as a predictable treatment modality. However, there is a lack of knowledge among the general population about the prevalence and need for implant treatments. PURPOSE: This study was undertaken to register and compare the prevalence of dental implants and the subjective need for implant treatment among people in Sweden and Denmark. MATERIALS AND METHODS: Random samples taken from the national population registers in Sweden and Denmark comprised 1001 Swedish subjects aged 55 to 79 years and 1175 Danish subjects aged 45 to 69 years. Subjects were requested to fill out questionnaires regarding dental conditions, subjective need for implant treatment, whether they had received treatment with dental implants during the previous 10 years, and so on. RESULTS: Of the Swedes, 4.8% reported that they had dental implants, compared with 2.5% of the Danes. In the Swedish sample, age was significantly associated with subjective need for implant treatment. In the Danish sample, women showed a significantly higher subjective need for implant treatment than did men. CONCLUSIONS: Compared with the Swedish sample, the subjective need for treatment with dental implants was higher in the Danish sample, although the patient fees were substantially higher in Denmark.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".