An Assessment of Dental Treatment Need: An Overview of Available Methods and Suggestions for a New, Comparative Summative Index
Bibliographic record
Abstract
OBJECTIVE: The aims were to give an overview and consider advantages and disadvantages of different approaches used to evaluate dental treatment need and to suggest an alternate Quantitative Summative Dental Treatment Need Index. METHODS: The Medline Ovid database was searched for relevant articles published during the last three decades combining the terms "needs assessment," "dental care," "health services needs and demand." RESULTS: There were substantial differences in methods used. Different modifications of the Decayed, Missing, Filled Teeth/Surfaces indices, complex quantitative summative indices, or simplified approaches were used to assess dental treatment need. Differing advantages and disadvantages of these methods can be identified. Previously used approaches have a common limitation for use in oral epidemiology. CONCLUSIONS: The suggested alternate Quantitative Summative Dental Treatment Needs Index focuses on an ability to compare both the total burden of treatment need as well as to make a distinction among specific treatment needs across populations. This new approach is an attempt to develop a comprehensive index for use in oral epidemiology with further revisions anticipated.
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 | 0.113 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.053 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".