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Record W2969973443 · doi:10.1093/asj/sjz015

Commentary on: The Public’s Perception on Breast and Nipple Reconstruction: A Crowdsourcing-Based Assessment

2019· letter· en· W2969973443 on OpenAlexaff
Jamil Ahmad, Frank Lista

Bibliographic record

VenueAesthetic Surgery Journal · 2019
Typeletter
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCrowdsourcingPerceptionBreast reconstructionMEDLINEInternal medicineBreast cancerWorld Wide WebEpistemology

Abstract

fetched live from OpenAlex

It is with great pleasure that we discuss “The Public’s Perception on Breast and Nipple Reconstruction: A Crowdsourcing-Based Assessment” by Azadgoli et al.1 In this article, Dr Azadgoli and colleagues attempted to assess the general population’s opinion on breast and nipple reconstruction utilizing crowdsourcing. General preferences about breast reconstruction were investigated, but, more specifically, the survey included a focus on preferences among the general population regarding nipple aesthetics. Crowdsourcing has been defined as “the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call. Crowdsourcing is the mechanism by which talent and knowledge is matched to those in need of it.”2,3 Crowdsourcing can increase the accuracy of computer automated tasks, lower costs, increase the scale of research, transcend boundaries and borders, produce novel discoveries, and increase the speed of research progression.3 It is an appealing approach to performing research because it has the potential to massively increase the sample size while substantially lowering costs.3 Although interest in the application of crowdsourcing in health is relatively recent, it has been widely used across medical disciplines.4 As Azadgoli et al1 noted, there are several plastic surgery studies that have used crowdsourcing to obtain data. Vartanian et al5 used the internet crowdsourcing service Amazon Mechanical Turk to distribute a survey to characterize features of the ideal thigh and relationships between the thighs and buttocks. Using internet crowdsourcing, they were able to recruit 1034 responses. Bucknor et al6 used the same internet crowdsourcing service to evaluate public preferences for a plastic surgeon’s gender and demeanor. They received 341 responses. Nayyar et al7 also utilized Amazon Mechanical Turk to distribute a survey to analyze the preferences of patients seeking 3 common aesthetic procedures: breast augmentation, facial rejuvenation, and combined breast/abdominal surgery. Participants were surveyed about their preference for specific social media platforms, extent of information provided, delivery mechanism, type of messenger, and interactivity. They were able to recruit 647 participants. In all of these studies,5-7 the authors were able to obtain a significant amount of data in a relatively short time and for a relatively low cost. In this current study, Azadgoli et al1 distributed a survey through Amazon Mechanical Turk. The survey included questions assessing participant demographics, personal experience with breast reconstruction, perceptions on breast reconstruction, and opinions regarding aesthetic results. A total 992 responses were collected within 3 months; most participants were female (56.1%), white (32.1%), and in their 30s (40.4%) and had completed an undergraduate degree (42.0%). The majority of the population (84.3%) preferred the results of nipple-sparing mastectomy to reconstructed nipples (15.6%), and the results of 3D tattooed nipples (57.5%) were preferred over reconstructed nipples (42.5%) as well. The authors noted some significant differences in preference of nipple aesthetics based on gender, age, ethnicity, and education level of the respondents. The preference for nipple-sparing mastectomy over nipple reconstruction was universal across age and education groups. Compared with nipple-sparing mastectomy, even fewer men preferred nipple reconstruction than women. Across the various ethnic groups, only 15.6% preferred nipple reconstruction to nipple-sparing mastectomy. When nipple reconstruction was compared with 3D tattooed nipples, significantly more men preferred 3D tattooed nipples compared with women. The majority of the population (71.0%) had the opinion that a breast without a nipple is incomplete. To achieve a complete nipple-areola complex, the vast majority respondents (91.9%) would be willing to undergo additional surgeries. However, it appeared that the willingness to undergo further surgery decreased with increasing age. Importantly, most respondents would be willing to undergo an increased number of procedures to improve the chance of nipple survival (69.4%) and to improve the aesthetic results (65.0%). The findings of this study again underscore the importance of aesthetic results in breast reconstruction and the particular importance of the nipple-areola complex in contributing to these results. Both nipple-sparing mastectomy and 3D tattooed nipples were preferred over nipple reconstruction. This begs the question of what feature(s) of the nipple-areola complex have a greater impact on our perception of breast beauty: What is more important: nipple projection, nipple size, areolar color, areolar texture, or areolar diameter? Beyond the ideal position of the nipple-areola complex on the breast,8 ideal aesthetics of the nipple-areola complex have not been well studied. Regardless, all 3 approaches result in a nipple-areola complex after mastectomy, and, if possible, patients should be presented with these options because they may have a preference of one approach over the others. Whereas most studies reporting outcomes after aesthetic and/or reconstructive breast surgery report the surgeons’ or patients’ experiences, the authors used crowdsourcing as a way of examining the general population’s opinion. However, it is important to note that in this study, 44.2% had personal experience with breast reconstruction and 25.8% with nipple reconstruction. Although it remains unspecified what the respondents’ personal experiences actually were, this may have added bias to their responses when compared with the general population who do not have any personal experience with these procedures. As in all areas of research that involve sampling from a given population, one of the important issues arising in utilizing crowdsourcing for health research is ensuring that the respondents have the appropriate characteristics required for the specific task. Another limiting factor of this study may be the quality of the example used to represent each technique (ie, nipple-sparing mastectomy, nipple reconstruction, 3D nipple tattooing) that was shown to those surveyed. The scar extending onto the breast for the patient used as an example of nipple reconstruction is darker and more apparent than the scars on the breasts of the patients used as examples of nipple-sparing mastectomy and nipple reconstruction. This may affect the respondents’ perception of the result more than if a patient with lighter scars was included as an example of a nipple reconstruction. Using more than one example for each technique would provide more robust data and reduce this confounding factor; however, we recognize that this would make for a much longer survey and potentially reduce the number of responses. In summary, this article provides information about breast and nipple reconstruction from the perspective of the general population and offers a different perspective from that of the plastic surgeon and patients. It reconfirms the impact of aesthetics on our perception of the quality of the result. This study also brings attention to the importance of the nipple-areola complex on the result. This study adds to the growing literature using crowdsourcing in healthcare research, and it remains to be seen what utility crowdsourcing will have in plastic surgery and medicine in general. The authors declared no potential conflicts of interest with respect to the research, authorship, and publication 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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.063
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0630.041
Insufficient payload (model declined to judge)0.0100.006

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.

Opus teacher head0.079
GPT teacher head0.337
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2019
Admission routes1
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