Welcome to this June twenty twenty-six rundown from Dermatologic Surgery. There's one article on the docket this time, and it's a meaty one, so we're going to spend real time with it rather than rushing through a stack. It's a systematic literature review out of the aesthetic medicine space, led by Shannon Humphrey and colleagues, several of whom are affiliated with Allergan Aesthetics and AbbVie — worth flagging that industry funding up front, since it's relevant when we get to interpretation. The title is "Assessment of Methods and Attributes Used to Characterize Skin Quality: A Systematic Literature Review," and even though most of us think of skin quality as a soft, almost marketing-adjacent term, this paper makes a pretty compelling case that the lack of rigor around it has real downstream consequences.

Let's start with the problem the authors are trying to solve. Skin quality is the umbrella term patients use when they say they want skin that looks youthful, glowing, or healthy — but as any of us who do cosmetic consults alongside our Mohs and reconstructive work know, "skin quality" is doing an enormous amount of conceptual labor without much definitional discipline behind it. Attributes like elasticity, laxity, dullness, radiance, crepiness, and pore visibility get used almost interchangeably across studies, sometimes even within the same paper, and there's no consensus on what any of them actually mean operationally. Humphrey's group had already proposed one classification framework back in twenty twenty-one, organizing skin quality into visual, topographical, and biomechanical domains, and a separate group independently proposed something similar using categories like tone evenness, surface evenness, firmness, and glow. The convergence of two independent frameworks is interesting, but neither has been widely adopted, and the authors wanted to quantify just how fragmented the underlying literature really is — essentially auditing two decades of aesthetic medicine research to see whether we're even measuring the same things when we claim to study the same attribute.

Now the methods, because the structure here is genuinely instructive if you ever find yourself appraising or conducting a systematic review yourself. They ran two linked reviews. The first was a broad systematic search across PubMed, MEDLINE, and Embase, using PRISMA methodology, looking for any study published from January two thousand through the end of twenty twenty-one that assessed skin using either an objective instrument — things generating reproducible quantitative output, like wrinkle counts or pore measurements — or a subjective instrument, meaning expert observer scoring, at two or more timepoints. That two-timepoint requirement is a deliberate methodological choice: it filters out purely descriptive or cross-sectional work and keeps only studies actually capturing change, which is what you need if you're trying to understand how an attribute is operationalized in the context of treatment response. The second piece was a supplemental search, run later in twenty twenty-four, restricted specifically to meta-analyses published from twenty ten onward, and this one was narrower by design — limited to seven specific attributes mapped onto their proposed framework: dry skin, dullness, hyperpigmentation, visible pores, wrinkles, laxity, and crepey skin. The authors explain this restriction to meta-analyses explicitly: they wanted more internal consistency than you'd get pooling individual heterogeneous trials, so meta-analyses served as an already-aggregated, somewhat pre-filtered evidence base for treatment outcomes. On top of the two formal reviews, they also did a keyword sweep of gray literature — FDA and Chinese State FDA documents, professional society materials, academic sources, even blogs and media — which is unusual for a Dermatologic Surgery paper but makes sense given their goal wasn't just academic; they wanted to see how these terms are actually being used in real-world clinical and commercial communication with patients.

Screening followed standard dual-reviewer methodology with a third adjudicator for disagreements, and data extraction was checked in the same fashion, all organized into a queryable database — again, appropriate rigor for what is essentially a definitional audit rather than a therapeutic comparison.

Now the results, and this is where the paper's core message lands. The initial search started with about forty-seven hundred unduplicated records, and after PICOS screening and full-text review, whittled down to nine hundred and three studies that made it into final extraction — yielding nearly forty-seven hundred individual observations, since many studies reported on multiple attributes or timepoints. Geographically this was a genuinely global literature, spanning over fifty countries, though skewed — roughly a quarter of studies from the United States, with South Korea, China, Italy, and Brazil rounding out the next tier, each contributing somewhere in the mid-single digits to low double digits percentage-wise. The face dominated as the assessed body site, accounting for about eight in ten observations, which tracks with what we'd expect clinically.

Here's the headline finding: subjective endpoints — expert observer scoring rather than instrumented measurement — made up the overwhelming majority of assessments, at about eighty-seven percent of all observations. That's a substantial and clinically meaningful skew toward subjectivity in a field that likes to present itself as increasingly quantitative and device-driven. And then the real crux of the paper: when the authors looked at how individual attributes were actually defined across studies, they found no consensus whatsoever. For any given attribute, they identified anywhere from seven to seventeen distinct definitions in circulation. Aging and photoaging themselves, which together accounted for nearly half of all observations, were treated as composite terms rather than singular attributes, with descriptions pulling in wrinkling, dryness, texture, pigmentation, and elasticity in varying combinations — and even the directionality wasn't consistent, since some papers described an increase in signs of aging while others framed things as a decrease in those same signs, which is a subtle but important inconsistency if you're trying to compare outcome directions across studies. Photoaging fared similarly poorly, with variable emphasis on texture, pigmentation irregularity, topographic change, and laxity depending on which paper you picked up. The supplemental meta-analysis search, despite covering nearly four thousand initial records, ultimately yielded only twenty meta-analyses that met inclusion criteria across all seven targeted attributes combined — which itself tells you something about how thin the pooled, high-quality evidence base still is for something as commonly discussed as skin quality.

On to discussion and limitations, and this is a case where the authors are refreshingly candid because the entire point of the paper is to expose fragmentation rather than defend a particular treatment. Their central conclusion is that this absence of consensus definitions is not just an academic nuisance — it's a structural barrier to comparing treatment options head-to-head, and they explicitly raise the concern that it could be shaping physician recommendations and, downstream, patient satisfaction and outcomes. If two studies both claim to measure "elasticity" but use different constructs entirely, you cannot meaningfully say one intervention outperforms another for that attribute. As for limitations, the authors don't dwell heavily on their own methodology's weaknesses in a dedicated section, but a few things are worth naming ourselves. This is fundamentally a descriptive, definitional review — it cannot and does not tell us which treatments actually work best for any given attribute, only that the underlying terminology is too inconsistent to make that comparison rigorously. The heavy reliance on subjective endpoints across the included literature is a finding about the field, not a flaw the authors introduced, but it does mean the review is characterizing a subjective-heavy evidence base rather than a gold-standard instrumented one. There's also the elephant in the room of funding: this was sponsored by Allergan Aesthetics slash AbbVie, with several authors as employees or former employees, and while the group had previously published its own proposed framework, so there's an argument they had a stake in demonstrating that consensus is lacking and that their own framework might fill the gap. That doesn't invalidate the data, which is a fairly mechanical count of definitions across nearly a thousand studies, but it's context worth holding onto when reading the framing of the conclusions.

So what do you actually do with this as a Mohs surgeon and dermatologic oncologist who also runs a cosmetic practice, or refers patients into one? This is not practice-changing in the sense of altering a technique or a margin protocol — there's no procedural or oncologic content here at all. But it is genuinely useful as a conceptual corrective, particularly if you counsel patients on combination surgical and cosmetic care, or if you evaluate device or filler literature for use in your own practice. The practical takeaway is this: when you see a study or a manufacturer claim improvement in "skin quality," "radiance," or "elasticity," treat that language with real skepticism until you know exactly how the outcome was defined and measured, because the same word can mean seven to seventeen different things depending on which paper you're reading. It also reinforces something useful for patient communication — using precise, attribute-specific language with patients, rather than the umbrella term skin quality, likely sets more accurate expectations and makes your own outcome discussions more defensible. Nothing here demands you change a technique tomorrow, but it's a good nudge toward more disciplined vocabulary the next time you're evaluating a new energy device or injectable trial for your aesthetic patients.

That wraps our single deep dive for this June issue. Thanks for listening, and we'll be back with the next set of articles soon.