The Toronto outcome measure for craniofacial prosthetics: a condition-specific quality-of-life instrument.
Bibliographic record
Abstract
PURPOSE: The objective was to develop a patient-based outcome measure of condition-specific quality of life that would minimize measurement error related to the instrument when used with patients requiring extraoral craniofacial prostheses. MATERIALS AND METHODS: An item pool of potential questionnaire items covered 10 clinical/technical and social/psychologic domains. They sought how frequently the issue in the item affected patients and how important the problem in the item was. The 139 items were administered to 94 treated patients in 5 centers in the United States, Canada, and the United Kingdom. Items were eliminated using relevance (frequency x importance), frequency of answer endorsement, Cronbach's alpha (internal consistency), and correlation of items on the same subject. International cultural agreement was tested using analysis of variance and Tukey comparisons within each domain. Scoring was transformed to a scale (0 to 100). RESULTS: The final instrument contained 52 items yielding a mean quality of-life score of 72.5% and a standard deviation of 17.9. Very high internal consistency was demonstrated with a final Cronbach's alpha of 0.967. No international cultural disagreement was found in 9 of the 10 domains. DISCUSSION: The relative weight of each of the domains is (partially) based on the relevance to the patients. Of the 52 items, 29 were identified that do not mention a prosthesis. This subscale has a Cronbach's alpha of 0.976. These items may therefore be useful where within-patient change is of interest. CONCLUSION: A patient-based outcome measure of condition-specific quality of life has been developed with control of bias and demonstrated performance characteristics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".