Content Validity of Patient-Reported Outcome Instruments used with Pediatric Patients with Facial Differences
Bibliographic record
Abstract
OBJECTIVE: The aim of this systematic review was to identify patient-reported outcome (PRO) instruments used in research with children/youth with conditions associated with facial differences to identify the health concepts measured. DESIGN: MEDLINE, EMBASE, CINAHL, and PsycINFO were searched from 2004 to 2016 to identify PRO instruments used in acne vulgaris, birthmarks, burns, ear anomalies, facial asymmetries, and facial paralysis patients. We performed a content analysis whereby the items were coded to identify concepts and categorized as positive or negative content or phrasing. RESULTS: A total of 7,835 articles were screened; 6 generic and 11 condition-specific PRO instruments were used in 96 publications. Condition-specific instruments were for acne (four), oral health (two), dermatology (one), facial asymmetries (two), microtia (one), and burns (one). The PRO instruments provided 554 items (295 generic; 259 condition specific) that were sorted into 4 domains, 11 subdomains, and 91 health concepts. The most common domain was psychological (n = 224 items). Of the identified items, 76% had negative content or phrasing (e.g., "Because of the way my face looks I wish I had never been born"). Given the small number of items measuring facial appearance (n = 19) and function (n = 22), the PRO instruments reviewed lacked content validity for patients whose condition impacted facial function and/or appearance. CONCLUSIONS: Treatments can change facial appearance and function. This review draws attention to a problem with content validity in existing PRO instruments. Our team is now developing a new PRO instrument called FACE-Q Kids to address this problem.
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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 | 0.044 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".