An electronic oral health record to document, plan and educate
Bibliographic record
Abstract
The University of British Columbia (UBC) in collaboration with a software developer (Planmeca Oy, Finland) created an electronic oral health record based on the principles of cognitive ergonomics rooted in the European research and development project 'ORQUEST' to guide students through medical, dental, social histories, examinations, treatment planning and progress notes. Clinicians in each dental specialty of the Faculty of Dentistry and software engineers cooperated to define the clinical content and workflow of clinical procedures in three phases: (i) development of a radiographic module, (ii) development of medical, dental, social and family histories, intra- and extra-oral examinations, progress notes and treatment planning and (iii) development of the orthodontic section accompanied by an optimisation phase to correct technical problems and clinical content issues. From a practical perspective, this EOHR enhances the clinical performance of students and the quality assurance capacity of the institution. It facilitates audits of clinical productivity and research, and it can be modified with relative ease to suit similar educational and clinical environments in either public or private healthcare settings.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.023 |
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".