Designing a framework of intelligent information processing for dentistry administration data.
Bibliographic record
Abstract
OBJECTIVES: This study was designed to test a cumulative view of current data in the clinical database at the Faculty of Dentistry, Dalhousie University. We planned to examine associations among demographic factors and treatments. METHODS: Three tables were selected from the database of the faculty: patient, treatment and procedures. All fields and record numbers in each table were documented. Data was explored using SQL server and Visual Basic and then cleaned by removing incongruent fields. After transformation, a data warehouse was created. This was imported to SQL analysis services manager to create an OLAP (Online Analytic Process) cube. RESULTS: The multidimensional model used for access to data was created using a star schema. Treatment count was the measurement variable. Five dimensions--date, postal code, gender, age group and treatment categories--were used to detect associations. Another data warehouse of 8 tables (international tooth code # 1-8) was created and imported to SAS enterprise miner to complete data mining. Association nodes were used for each table to find sequential associations and minimum criteria were set to 2% of cases. Findings of this study confirmed most assumptions of treatment planning procedures. There were some small unexpected patterns of clinical interest. Further developments are recommended to create predictive models. CONCLUSIONS: Recent improvements in information technology offer numerous advantages for conversion of raw data from faculty databases to information and subsequently to knowledge. This knowledge can be used by decision makers, managers, and researchers to answer clinical questions, affect policy change and determine future research needs.
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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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".