Response to "The FACE-Q: The Importance of Full Disclosure and Sound Methodology in Outcomes Studies"
Bibliographic record
Abstract
We are writing in response to Dr Swanson's letter “FACE-Q and the Importance of Full Disclosure and Sound Methodology in Outcomes Studies.”1 We were disappointed Swanson found so little positive to say about a research program that was shaped both by the involvement of dozens of clinicians and the opinions and voices of hundreds of patients. We developed the FACE-Q in a transparent, scientifically rigorous fashion through the application of sound methodology and are confident that the FACE-Q provides a valuable tool for the advancement of outcomes research and evidence-based practice in aesthetic surgery.2 Patient-reported outcome (PRO) instruments are increasingly deployed as part of significant clinical decisions. Such assessment tools require serious attention and careful development to ensure that they generate valid, reliable data.3 This approach is generally understood by researchers, clinicians, policy makers, and governments.4–6 The methodology for developing PRO instruments not only is well established but also has been created mainly outside the plastic surgery field. In developing the FACE-Q, we have meticulously followed internationally accepted guidelines.7 Our study design, patient recruitment processes, and analysis techniques are accordingly appropriate,8 and all our work has been strictly peer-reviewed, beginning with grant submission through publication. As we acknowledged in our article, our study has certain limitations; however, many concerns raised by Swanson regarding methodology (consecutive patients, selection bias, confounders, etc) are misguided. These terms refer to a clinical study's validity, where validity—internal and external—is a technical term related to the magnitude of bias.9 The term validity when pertaining to a PRO instrument refers primarily to content validity10; as Swanson points out, content validity is the degree to which an outcome instrument actually measures the construct it is designed to measure. These 2 concepts of validity are quite different, as are the methodologies for the design of a clinical study versus development of an outcome measure.11,12 The reader should not be misled by the inclusion of terms from one methodology (clinical trials) when, really, we are concerned with the other (outcome measure development). We went to great lengths to ensure the content validity of the FACE-Q through a process that involved in-depth interviews with patients and extensive expert panels.13,14
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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 | 0.019 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.034 | 0.050 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".