Psychologic Outcomes in Implant Prosthodontics
Bibliographic record
Abstract
Francesco Bassi, MD, DDS/Alan B. Carr, DMD/Ting-Ling Chang, DDS/Emad W. Estafanous, BSD, MSD/Neal R. Garrett, PhD/Risto-Pekka Happonen, DDS, PhD/Sreenivas Koka, DDS, MS, PhD, MBA/Juhani Laine, DDS, PhD/Martin Osswald, MBDS, MDent/Harry Reintsema, DDS, PhD/Jana Rieger, MSc, PhD/Eleni Roumanas, DDS/Thomas J. Salinas, DDS, MS/Clark M. Stanford, BSc, DDS, PhD/Johan Wolfaardt, BDS, MDent, PhD: Consensus regarding outcomes of the treatment of tooth loss, especially the psychologic outcomes, is needed to guide discovery of best practices and enable a better understanding of patient management for this chronic condition. This paper presents the findings of the ORONet Psychological Working Group for prosthodontics and aims to identify psychologic outcomes with properties deemed critical to meet clinical trial and clinical practice needs for the future. References obtained using a PubMed/Medline search were reviewed for clinical outcomes measures of interest. Clinical outcomes measures were judged relative to the criteria of truth, discrimination, and feasibility. Of the psychologic outcome measures identified in this systematic review, only the OHIP-14 was thought to be suitable for use in general practice and multi-institutional outcome registries and clinical trials. Development of clinically useful psychologic outcomes for future use could benefit from developmental methods and tools outlined in the patient-related outcomes field of clinical care. Int J Prosthodont 2013;26:429Â434. doi: 10.11607/ijp.3403
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".