Bibliographic record
Abstract
In the last three decades, the arbitrary declaration of a successful result by physicians or the use of physiological measures for health-related interventions has been supplanted by patient-reported outcomes (PROs). This has contributed a lot to the evidence-based medicine (EBM) movement, with EBM been heralded by the British Medical Journal as one of the top 15 advances in healthcare in the last 150 years.1 For readers who may not be fully familiar with PROs, unlike typical, surgeon derived measure of outcome (“my patients had XX% complications, everybody was satisfied”), PROs look at outcomes from the patient perspective, in this case how—and by how much—body contouring surgery changed their quality of life (QOL). Over the years specific guidelines have been published into how QOL should be measured. After a surgical intervention it is best measured with a combination of scales including a generic scale, a condition specific scale (such as the PRO used in the present article) and a utility scale.2 Generic health-related quality of life (HRQL) scales allow the comparison of health related quality of life among patients with different types of diseases or conditions but may not be sensitive enough to detect small differences in patient groups with specific conditions, for example redundant skin after bariatric surgery. A condition specific scale, such as the body-QOL, helps define these differences. In this EBM Hub we will use the Suijker et al article3 as springboard to expand on some important issues of measurement of the quality of life (QOL) of patients after aesthetic surgery. They describe their study as a phase IV report of the results of body contouring surgery (BCS) in patients who underwent bariatric surgery with massive weight loss (MWL) using the body-QOL. The same authors previously reported on the development of this PRO which they labeled as, the body-QOL. They claim it is the first to measure BCS.4,5 Since their initial publication of the body-QOL, another PRO has been published by Klassen et al the so-called BODY-Q which covers all the stages of the bariatric surgery journey; bariatric surgery to body contouring surgery in its various anatomical areas.6 The development of a PRO is an arduous task that can take years, involving many investigators with clinical epidemiology, psychology, and statistical backgrounds. In the introduction section the authors mention that “To our knowledge, it is the first instrument for this purpose designed in accordance with international guidelines for creation of PROMs.” Being of the critical analysis type, we investigated further this claim by reviewing their previous publications on the development of this PRO.4,5 We are happy that the methods used in the development of the body-QOL followed correct methodology. We congratulate Suijker et al for this important work which has immediate applicability in the expanding area of BCS. We will not be harsh on the authors as we like their PRO, but we will identify some areas which should have been made clearer. The body-QOL developed by Danilla et al is anatomically specific; to the abdominal area. The Suijker et al article does not cover the full spectrum of BCS. A different title of the article such as “Long-Term Quality-of-Life Outcomes after abdominal contouring using Body-QOL,” would have keyed the reader to the anatomical specificity of the findings, rather than a more global implied anatomy. An important element of a QOL scale is the concept of minimal clinical important difference (MCID). The MCID is the change of a score in a QOL scale that is considered meaningful to patients. Let’s say, for example, that the change in score in the body-QoL from preoperative to postoperative was 5 points in a study. We must ask ourselves “is this a meaningful change?” “Would the patients consider this change important enough to submit themselves to BCS?” It behooves the scale developers to be explicit what the MCID is in their advocated PRO. While unfortunately Suijker et al do not mention a MCID for this study, they do inform us that “the original scale goes from 20-100.” The adjusted body-QOL goes from 0 to 100. Generally speaking, in scales that go from 0 to 100, a change of 10 points is considered clinically important. Looking at Table 4 of their article,3 we see that the preoperative body-QoL score of their population was 44.0 ± 14.1 and postoperative was 85.5 ± 17.5. The difference is 41.5 points which is 4 times our extrapolated MCID, which would signify a huge improvement in patient QOL. This improvement is consistent in all subscales of the body-QOL (body satisfaction, sex life, self-esteem and social performance, physical symptoms). In the results section, the authors should have shown us a flow diagram to include patients who had bariatric surgery, patients who had BCS and patients who completed the questionnaire. We constructed a flow diagram from their reported data (Figure 1). An important denominator missing from the diagram is the number of patients who had bariatric surgery. This is important to know as despite our claim of helping patients with BCS, from a societal point of view we only help a very small number (mostly due to financial considerations). While it is virtually impossible to have 100% of patients follow through all stages of a study, it is important for authors to do their best to follow at least 80% of them. Appropriately, the authors compared, as best they could, the responders and non-responders. The authors’ Table 13 shows demographic characteristics of entire study population (n = 112) and responders to all assessments (n = 44). They showed no statistical difference. This however does not mean that the QOL of nonresponders was the same! It is conceivable that nonresponders may have had a poor result and hence did not complete the body-QOL questionnaire. For example, satisfied, happy patients might have been more prone to respond than dissatisfied patients, however there is no way to tell. This is why it is so important for authors to work hard to get as high a response rate as possible . . . the higher the response rate, the more this kind of question fades away. Flow diagram of study patients. For the PROs to be useful to clinical investigators, patients and society in general, they need to satisfy the important psychometric properties of validity, reliability, and responsiveness to change. It is therefore important for aesthetic surgeons to look for mention of these properties in the introduction or methods of any PRO that purports to measure the QOL of an aesthetic intervention. If the authors had reminded the readers of what specific indicators were being measured, the typical reader would be better informed. We looked specifically in this article to find explicit mention of the psychometric properties of this PRO. Unfortunately, there was no explicit mention of these in either the introduction or methods section. Although it was not Suijker et al’s intention to cover the general topic of different approaches of measuring QOL, we will expand on this for the readers as it is relevant to the subject matter of how we should be measuring BCS. Quality of Life after a surgical intervention should be measured with a combination of scales which should include a generic, a condition specific scale (such as the PRO used in the present article) and a utility scale.2 HRQL scales allow the comparison of health related quality of life among patients with different types of diseases or conditions but may not be sensitive enough to detect small differences in specific patient groups with specific conditions (eg, redundant skin after bariatric surgery). We are a bit surprised, however, by the authors’ claim “Other generic instruments like SF-36 {18}… have been used and failed to demonstrate improvement of QoL after BCS on MWL populations.” We have reviewed this specific article by Brazier et al7 and there is no mention that they assessed BCS on MWL. Furthermore we will be very surprised if the SF 36 did not capture such a large effect change in this population. Utility scales provide preference-weighted outcomes measures that express patients’ preferences for a particular given health state relative to death. Death as represented by 0 and perfect health is represented by 1. There are different ways for calculating utilities such as, visual analogue scales (VAS), standard gamble (SG), time-trade-off (TTO) and standardized questionnaires such as the EQ-5 D and the health utilities index (HUI). The advantage of the Utilities is that they can be used to calculate quality qdjusted life years (QALYs)8 an important component of cost-effectiveness analysis. If in the future clinical investigators decide to compare the cost-effectiveness of one BCS technique to another the only reasonable way to perform a full economic evaluation is to measure the utility of the pre- and postintervention. Going forward, we will undoubtedly be seeing more and more PRO instruments for plastic surgery and more and more research using these instruments. The statistical jargon used in the development and reporting of the validation of these PROMs in the various development phases, however, can be daunting to the average surgeon. Surgeons should not be intimidated by the arcane language used. There are other very important features other than those related to validation of a scale that need to be considered in the measurement of the QOL after a surgical intervention. We encourage aesthetic surgeons to be familiar with the appraisal of articles that appear in the surgical literature that purport to be a health related QOL article. It can be easily demystified.9 The authors have no conflict of interests to disclose related to the content of this article. The authors received no financial support for the research, authorship, and publication of this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".