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He’s the Frenchest man since Gainsbourg, she’s the world’s premier burlesque star. Together, Sebastian Tellier and Dita Von Teese have made an unlikely electronic pop collaboration worthy of Serge himself

Alexis Petridis

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S eated together in an upscale London bar, Plexi 85 patent leather pumps Gianvito Rossi 5gXS2Cyn
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are a study in contrasts. She is immaculately turned out and looks, well, exactly like Dita Von Teese , from her perfectly coiffed jet-black hair to her seamed stockings and high heels. He is the picture of bohemian dishevelment: wild beard, straggly hair, a large glass of wine in one hand and packet of fags and a lighter in the other.

She exudes a very US kind of retro glamour, and talks about her transformation from Heather Sweet, a blond, machinist’s daughter from Michigan, into the world’s best-known burlesque star in terms of the American dream: “Like the big Hollywood makeover, a girl would come from a Midwestern town and she’d look like an ordinary girl, and then suddenly she’d be otherworldly – I wanted to do that in real life.”

Tellier, on the other hand, is so extraordinarily Gallic I wonder if he isn’t putting it on a bit, in the same way that the late Serge Gainsbourg was fond of cranking up the hon-he-hon for his own amusement if a British journalist was in earshot. I don’t think he is, but, nevertheless, were he any more French he would probably nip out in the middle of the interview to have an affair with my wife: he makes the philosophising maître d’ on First Dates look like Eric Bristow . “Ah, Diteuuuuur! Beautiful! So beautiful!” he cries on arrival, in lieu of the more traditional “hello”.

His voluble answers to questions tend towards the metaphysical. When I ask about the songs on Dita Von Teese, the album the pair have made together – Tellier and his wife, Amandine de la Richardière, are responsible for the music and lyrics – he tells me they are about “attraction and desire”, and then starts talking about the cosmos.

“It’s like in the universe, in space, you have the planet, and it moves around the star, no? It creates a kind of ballet. And I try to do the music of this ballet through Dita. Composing the music, I was dreaming of Dita, a Dita of the stars. Dita is not a woman but a planet. It’s true, no? Also, I like paradoxes, so when the lyrics are happy, the notes are sad, and when the lyrics are sad, the notes are happy. So it’s never totally sad, it’s never totally happy. It’s like the life, yes? Something complicated.”

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And now, the little princesses are celebrating their first birthdays with a reunion, complete with tiny dresses and new headbands.

"When we finished the shoot last year, exhausted from photographing six babies at once, my staff and I sat back and laughed and said, 'It will be so much easier next year when they are all one!'" Karen said. "At that point, I knew I was going to do the shoot. It then became such an exciting time when the newborns went viral. Over 17 million views on Facebook and the story was ran by over 50 different news agencies."

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Karen, who has two daughters and a son, started Belly Beautiful Portraits nearly 12 years ago, but has studied photography since high school. Disney has always been a part of her life.

"The Disney Princesses have always been a source of inspiration to me," she said. "My mother and my grandmother would read their stories and watch the movies with me. These amazing tales of love and courage have always resonated with me."

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However, Karen's love for taking care of babies stems from her time working with mothers and newborns.

"I'm a former labor and delivery nurse. My entire career has been about babies," she said. "After I had my son, my mother-in-law said, 'Pictures are all we have.'"

++PREVIOUS STORY: Disney princesses come to life in once-in-a-lifetime photo shoot

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but Karen said she had been wanting to do something like this for a while.

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"I've had this idea in my head for so long," she said. "My staff and the lady who makes the gowns all sort of came together to create this six sets."

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, created the gowns.

Karen and her team created the sets and put out a call on social media for six baby girls. Fifty parents applied.

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"Originally, we had nine babies who were supposed to be there, so we had backups, but that day, we only had six. So even if the babies were crying, they had to be the princesses," she said.

Having finished her second major Disney princess photo shoot, Karen is set to travel to Brazil in a few weeks to teach about creative newborn photography.

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Advances in health information technology, such as electronic health records (EHRs) and disease registries, promise new sources of evidence on the effectiveness of health interventions. As these data sources become more readily accessible to investigators, they are likely to supplement or even replace clinical trials data in SRs of

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CER. Furthermore, as with other data sources, the potential for bias and confounding will need to be addressed.

The Food and Drug Administration Sentinel Initiative and related activities (e.g., Observational Medical Outcomes Partnership) may be an important new data source for future SRs. When operational, the Sentinel Initiative will be a national, integrated, electronic database built on EHRs and claims records databases for as many as 100 million individuals (HHS, 2010; Platt et al., 2009). Although the principal objective of the system is to detect adverse effects of drugs and other medical products, it may also be useful for SRs of CER questions. A “Mini-Sentinel” pilot is currently under development at Harvard Pilgrim Health Care (Platt, 2010) . The system will be a distributed network, meaning that separate data holders will contribute to the network, but the data will never be put into one common repository. Instead, all database holders will convert their data into a common data model and retain control over their own data. This allows a single “program” to be run (e.g., a statistical analysis in SAS) on all the disparate datasets, generating an estimated relative risk (or other measure) from each database. These then can be viewed as a type of meta-analysis.

The fact that available data are conducive to pooling is not in itself sufficient reason to conduct a meta-analysis (Fu et al., 2010). The meta-analysis should not be undertaken unless the anticipated results are likely to produce meaningful answers that are useful to patients, clinicians, or other decision makers. For example, if the same outcomes are measured differently in the individual studies and the measures cannot be converted to a common scale, doing a meta-analysis may not be appropriate (Cummings, 2004). This situation may occur in studies comparing the effect of an intervention on a variety of important patient outcomes such as pain, mental health status, or pulmonary function.

Conducting the Meta-Analysis

Good statistical analyses quantify the amount of variability in the data in order to obtain estimates of the precision with which estimates may be made. Large amounts of variability reduce our confidence that effects are accurately measured. In meta-analysis,

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variability arises from three sources—clinical diversity, methodological diversity, and statistical heterogeneity—which should be separately considered in presentation and discussion (Fu et al., 2010). Clinical diversity describes variability in study population characteristics, interventions, and outcome ascertainments. Methodological diversity encompasses variability in study design, conduct, and quality, such as blinding and concealment of allocation. Statistical heterogeneity, relating to the variability in observed treatment effects across studies, may occur because of random chance, but may also arise from real clinical and methodological diversity and bias.

Assessing the amount of variability is fundamental to determining the relevance of the individual studies to the SR’s research questions. It is also key to choosing which statistical model to use in the quantitative synthesis. Large amounts of variability may suggest a poorly formulated question or many sources of uncertainty that can influence effects. As noted above, if the individual studies are so diverse in terms of populations, interventions, comparators, outcomes, time lines, and/or settings, summary data will not yield clinically meaningful conclusions about the effect of an intervention for important subgroups of the population (West et al., 2010).

In general, quantifying heterogeneity helps determine whether and how the data may be combined, but specific tests of the presence of heterogeneity can be misleading and should not be used because of their poor statistical properties and because an assumption of complete homogeneity is nearly always unrealistic (Higgins et al., 2003). Graphical representations of among-study variation such as forest plots can be informative ( Figure 4-1 ) (Anzures-Cabrera and Higgins, 2010).

When pooling is feasible, investigators typically use one of two statistical techniques—fixed-effects or random-effects models—to analyze and integrate the data, depending on the extent of heterogeneity. Each model has strengths and limitations. A fixed-effects model assumes that the treatment effect is the same for each study. A random-effects model assumes that some heterogeneity is present and acceptable, and the data can be pooled. Exploring the potential sources of heterogeneity may be more important than a decision about the use of fixed- or random-effects models. Although the committee does not believe that any single statistical technique should be a methodological standard, it is essential that the SR team clearly explain and justify the reasons why it chose the technique actually used.

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FIGURE 4-1 Forest plot.

SOURCE: Schriger et al. (2010).

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In meta-analyses, the amount of within- and between-study variation determines how precisely study and aggregate treatment effects are estimated. Estimates of effects without accompanying measures of their uncertainty, such as confidence intervals, cannot be correctly interpreted. A forest plot can provide a succinct representation of the size and precision of individual study effects and aggregated effects. When effects are heterogeneous, more than one summary effect may be necessary to fully describe the data. Measures of uncertainty should also be presented for estimates of heterogeneity and for statistics that quantify relationships between treatment effects and sources of heterogeneity.

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  • Aaron Barnhart

    Medium member since Apr 2018

    Get my “52 History Films You Must See” free at www.historyispower.blog - speaker journalist media/film critic based in KC

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