Use of the Derriford Appearance Scale 59 to assess patient-reported outcomes in secondary cleft surgery
Bibliographic record
Abstract
Background Secondary rhinoplasty, one of the final procedures in addressing the stigma of the cleft lip and palate (CLP), has both functional and aesthetic objectives. The way in which physicians evaluate outcomes in surgery concerning aesthetics is changing. Well-designed patient-reported outcome measures to assess health-related quality of life improvements attributable to surgery are increasingly being used. The Derriford Appearance Scale 59 (DAS-59) is currently the only available validated patient-reported outcome measure that assesses concern about physical appearance. Methods Twenty patients with CLP presenting between May 2009 and May 2013 for secondary rhinoplasty to Sunnybrook Health Sciences Centre (Toronto, Ontario) were recruited. DAS-59 measures were administered both preoperatively and at least six months after surgery. Pre- and postoperative measures were scored and compared. Item-by-item analysis of the measure was also performed. Results Total scores for this CLP group indicated greater concern about appearance than the general population. Across all subscales of the measure, there was a reduction in scores after secondary rhinoplasty suggesting less patient concern with appearance and a positive effect of surgery on patient quality of life. Item-by-item analysis suggested relatively few items in the measure were driving overall change in total scores. Conclusion Comparison of pre- and postoperative scores with the DAS-59 in secondary cleft rhinoplasty suggests there is less concern with appearance after surgery. However, a small number of items within this generic scale contributing to this difference may suggest the need for a more patient specific measure for assessment of surgical outcomes in the cleft population.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".