A Review of Validated Quality-of-Life Patient-Reported Outcome Measures in Pediatric Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: There has been an exponential increase in the number of patient-reported outcome measures in plastic surgery. The authors reviewed the reliability, validity, and practicality of the most frequently used patient-reported outcome measures in pediatric plastic surgery research. METHODS: A review of the literature from January of 2010 to June of 2015 was conducted to identify patient-reported outcome measures in pediatric plastic surgery. Patient-reported measures used in five articles with two validation studies were included for analysis and classified as generic, disease-specific, and mental health. The type of validation used and reliability scores were compared across each class of outcome measure. The practicality of each measure was determined by the frequency of use and the number of items and cost. RESULTS: Of the 173 unique patient-reported outcome measures identified, 14 were included for analysis and classified as generic (n = 7), disease-specific (n = 4), and mental health (n = 3). The majority of all measures used construct validity. Disease-specific measures had the highest distribution of domains related to physical functioning, the same domain also found to have the highest reliability scores. A patient-reported outcome measure's frequency of use was not associated with its number of items or cost. CONCLUSIONS: This review found that generic patient-reported outcome measures were used most often, construct validity was used most frequently, physical functioning domains had the highest reliability, and the number of items or cost of a patient-reported outcome measure was not related to its frequency of use. Considered together, this information may inform the future development or selection of patient-reported outcome measures in pediatric plastic surgery.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".