Patients with reconstruction of craniofacial or intraoral defects: development of instruments to measure quality of life.
Bibliographic record
Abstract
Patients with reconstruction of craniofacial or intraoral defects experience a profound impact on their quality of life (QOL). This impact on QOL is influenced by the patients' medical conditions and the treatment interventions. Instruments to measure general QOL have been available for many years. A major criticism of QOL instruments is that too often the questions are not specific to the particular problems of a disease or condition. A search of the literature regarding QOL measurement for patients with maxillofacial implant-supported prostheses produced a short list of instruments, none of which were sufficiently developed or suited to the patients involved in reconstructive treatment. This study was designed to develop pretreatment and posttreatment questionnaires for measuring QOL for patients with reconstruction of a craniofacial defect and patients with reconstruction of loss of specific intraoral structures utilizing an implant-supported prosthesis (e.g., severe resorption of the maxilla or mandible or both). The goal was to develop brief, targeted instruments for this specific patient population. The produced instruments were sensitive and easy to administer and score, and no disruption of clinical care occurred with the administration of the questionnaires. The instruments were used with equal success both in face-to-face interviews and via mail.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".