Clinical Outcomes Measures for Assessment of Longevity in the Dental Implant Literature: ORONet Approach
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 Oral Rehabilitation Outcomes Network (ORONet) Longevity Working Group undertook a search of the literature from 1995 to 2009 on randomized controlled trials related to longevity of osseointegrated implants. Outcomes measures used in these studies were identified and subjected to the OMERACT component criteria of truth, validity, and feasibility. Through this process, it was a challenge to identify clinical outcomes measures that fully met the criteria. An attenuated version of the component criteria was applied, and clinical measures were identified for implant outcomes, prosthetic outcomes, and indices. A recommendation on standardized reporting periods was also presented for future consideration. The endpoint of the evaluation process is to develop consensus on clinical outcomes measures that can be applied across broad populations for osseointegrated implant care. The present ORONet initiative represents a beginning toward continual improvement and consensus development for clinical outcomes measures for osseointegrated implants. Int J Prosthodontics 2013;26:323Â330. doi: 10.11607/ijp.3402
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".