Oral Rehabilitation Outcomes Network—ORONet
Bibliographic record
Abstract
Francesco Bassi, MD, DDS/Alan B. Carr, DMD/Ting-Ling Chang, DDS/Emad Estafanous, BSD, MSD/Neal R. Garrett, PhD/Risto-Pekka Happonen, DDS, PhD/Sreenivas Koka, DDS, MS, PhD/Juhani Laine, DDS, PhD/Martin Osswald, BDS, MDent/Harry Reintsema, DDS, PhD/Jana Rieger, MSc, PhD/Eleni Roumanas, DDS/Thomas J. Salinas, DDS, MS/Clark M. Stanford, DDS, PhD/Johan Wolfaardt, BDS, MDent, PhD: The published literature describing clinical evidence used in treatment decisionmaking for the management of tooth loss continues to be characterized by a lack of consistent outcome measures reflecting not only clinical performance but also a range of patient concerns. Recognizing this problem, an international group of clinicians, educators, and scientists with a focus on prosthodontics formed the Oral Rehabilitation Outcomes Network (ORONet) to promote strategies for improving health based on comprehensive, patient-centered evaluations of comparative effectiveness of therapies for oral rehabilitation. An initial goal of ORONet is to identify outcome measures for prosthodontic therapies that represent multiple domains with patient relevance, are amenable to utilization in both institutional and practice-based environments, and have established validity. Following a model used in rheumatology, the group assessed the prosthodontic literature, with an emphasis on implantbased therapies, for outcomes related to longevity and functional, psychologic, and economic domains. These systematic reviews highlight a need for further development of standardized outcomes that can be integrated across clinical and research environments. Int J Prosthodont 2013;26:319–322. doi: 10.11607/ijp.3400
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.210 | 0.072 |
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".