Canadian Dental Patients with a Single-Unit Implant-Supported Restoration in the Aesthetic Region of the Mouth: Qualitative and Quantitative Patient-Reported Outcome Measures (PROMs)
Bibliographic record
Abstract
This article contains quantitative and qualitative patient-reported outcome measures (PROMs) collected from nine dental patients, with a single-implant in the maxillary anterior region of the mouth, recruited after obtaining consent documents. The quantitative data were obtained from participants’ demographics, frontal extraoral digital photographs, intraoral scans (IOS) of the maxillary arch, and self-administered questionnaires (where patients judged the overall, appearance, function, and comfort of their single-implant-supported crowns). Objective single-implant aesthetic index mean scores (Pink Esthetic Score/White Esthetic Score [PES/WES]) were obtained after two experienced calibrated clinicians analyzed the photographs and the three-dimensional models generated from the IOS. The self-administered questionnaires used a visual analogue scale (VAS) to obtain the patients’ subjective perceptions. The qualitative data were obtained from in-depth, semi-structured one-to-one interviews. The transcriptions from audio-recorded interview data were managed and coded, with the aid of a Computer-Assisted Qualitative Data Analysis Software (CAQDAS). These data were stored in a public repository that can be easily downloaded from a Mendeley data repository (DOI: 10.17632/sv8t6tkvjv.1).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".