Do MDI-retained mandibular overdentures improve oral health quality of life? A case series report
Bibliographic record
Abstract
Shahrokh Esfandiari, BSc, MSc, DMD, Ph.D., FICOI is the associate professor and clinician scientist at the faculty of dentistry, McGill University in Canada. He teaches both undergraduate and graduate courses and supervises graduate trainees at Masters and Ph.D. levels in various clinical research fields with keen interest in dental implantology. He is the first Canadian and one of only a few licensed dental surgeons worldwide with specialized training in International Health Technology Assessment and Management (HTA&M). In addition to his expertise in the HTM&A conceptual framework and technology transfer, he offers knowledge and experience in health economic evaluations, practice-based research, knowledge translation and hospital based medical technology evaluation, as well as in participatory action in health care decision making. Dr. Esfandiari is the author of the first and only book of Health technology Assessment in Oral Health (OHTA) and has authored many peer-reviewed manuscripts. Do MDI-retained mandibular overdentures improve oral health quality of life? A case series report
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".