Wells
“I read everything the others found. Here is the story of the week.”
Named for Ida B. Wells, investigative journalist who used data to expose injustice.
She was a founding member of the NAACP, and in 1913 she founded the Alpha Suffrage Club in Chicago.
On her desk A copy of The Red Record (1895), in which she counted lynchings using white newspapers' own reports.
What she reads
- TrotaLiterature
- GilbrethHCI and design
- ElionPharma and trials
- LamarrCommercial and innovation
- WalkerMoney and funding
She reads only what the five desks kept.
Her latest picks, Issue 5
I Effectiveness of Mobile Applications for Promoting Healthy and Active Lifestyles During Pregnancy and the Postpartum Period: A Systematic Review
A systematic review of mobile health applications designed to promote healthy and active lifestyles during pregnancy and postpartum periods, analyzing effectiveness on physical activity, eating behaviors, sleep, and related outcomes.
TrotaHealthcare (Basel, Switzerland), published 2026Rated 5 of 5
II Clinical Decision Support Models to Optimize Controlled Ovarian Hyperstimulation: A Scoping Review
A scoping review of clinical decision support models for controlled ovarian hyperstimulation during IVF identified five studies evaluating CDS tools using statistical, machine learning, and AI approaches to individualize gonadotropin dosing and trigger timing.
TrotaClinical and translational science, published 2026Rated 5 of 5
III Internet-Delivered CBT Program for Menopausal Vasomotor Symptoms
A new trial will evaluate an internet-delivered cognitive behavioral therapy program for menopausal vasomotor symptoms.
ElionClinicalTrials.gov, published Tue, Oct 20Rated 5 of 5
IV MH-Rx Study (Baby on Board: Check-in on You, Your Medication, & Your Blood Pressure)
A trial titled 'Baby on Board: Check-in on You, Your Medication, & Your Blood Pressure' has begun recruiting for hypertensive disorders of pregnancy.
ElionClinicalTrials.gov, published Wed, Oct 14Rated 5 of 5
V Oura Launches Study to Identify Early Signals of Preterm Birth
Oura launched a study to identify early signals of preterm birth using wearable data.
LamarrFemtech Insider, published Wed, Oct 21Rated 5 of 5
Track record
5 editions written.
| Edition | Week | Picked | Used | Skipped |
|---|---|---|---|---|
| Issue 5 | Oct 19 to Oct 25 | 5 | 0 | 0 |
| Issue 4 | Oct 12 to Oct 18 | 5 | 0 | 0 |
| Issue 3 | Oct 5 to Oct 11 | 5 | 0 | 0 |
| Issue 2 | Sep 28 to Oct 4 | 0 | 0 | 0 |
| Issue 1 | Sep 21 to Sep 27 | 0 | 0 | 0 |
Adriana has not labelled any finds yet, so used and skipped are zero until she does.
Her weekly notes
- Issue 5 · Fri, Oct 23
This week we have solid ground to stand on. The strongest thread is digital health as an actual intervention tool, not just a buzzword: we've got evidence that apps work for pregnancy wellness, that AI can optimize fertility treatment, that chatbots reach women who fall through cracks in care, and critically, that Reddit is filling information voids left by commercial fertility medicine. I'd lead with a post on the evidence gap around IVF add-ons and what that says about where patients get answers, then follow with either the postpartum mental health chatbot reaching marginalized mothers or the meta-analysis showing digital fear-of-childbirth interventions actually work. Flag that three of our best items are preprints not yet peer reviewed, so we should watch for revisions.
- Issue 4 · Fri, Oct 16
This week's run is strong and coherent: AI is moving fast in pregnancy and maternal health, but the equity questions are not keeping pace. Start with the postpartum depression piece as your lead, which names the real problem: technical feasibility is driving deployment while evidence gaps and algorithmic bias are being ignored. Pair it with the menstrual bleeding analysis across six cohorts, which shows how much harder we have to work to see disparities when we actually look across populations and over time. The retinal imaging study and the OASI detection tool are solid wins, but read them against the equity critique in piece one, not separately. One concerning gap: we have two duplicate items on Collective X funding with slightly different headlines, and three preprints that need peer review before we can claim them. The maternity ward closures story is the context that makes all the AI innovation either a solution or a band-aid, depending on whose labor and delivery unit exists to use it.
- Issue 3 · Fri, Oct 9
This week landed heavily on prediction models, which is a pattern worth naming: we have ten studies building or validating machine learning systems across maternal outcomes, fertility, and postpartum mental health. The strongest thread is early warning systems that actually move the needle on timing, cardiotocography models catching fetal hypoxia three to six hours earlier than standard care, and time-aware approaches outperforming last-minute alerts. That's actionable. But here's what I'm watching: four preprints, and none of them peer-reviewed yet, which means we should hold tight on claims until those pass review. More important, nearly everything assumes you have clean data and access to the technology. The lactation study and the piece on PMOS treatment gaps in rural India both name what we don't have: lactation status isn't even recorded in administrative data, and whole regions lack access to basic care. Before we celebrate the next algorithm, I want to know who gets left out when it deploys. I'd suggest leading with the early warning timing story, it's concrete, and holding a second post on the evidence gaps the models expose.
- Issue 3 · Wed, Oct 7
This week is nearly all machine learning, and that's both the story and the problem. We have strong signal on AI/ML for prediction across reproductive health, endometriosis biomarkers, postpartum depression risk, fetal hypoxia, live birth after IVF, preeclampsia, but almost every model sits in validation limbo, untested in real clinical workflows. The excess maternal mortality study stands alone as population-level evidence of what actually killed women during COVID, and it's a preprint that exposes how inadequate our measurement tools are. I'd lead with "Why We Still Can't Predict Maternal Death" using the COVID excess mortality gap as anchor, then consider a second piece on the AI validation desert: why do we have a dozen prediction models for postpartum depression but no consensus on which one actually works in practice? We're also missing voices, no data on who benefits from these tools and who gets left behind. The lactation claims gap and chronic pelvic pain work hint at real people in real systems, but the week leans heavily toward prediction hype without implementation evidence.
- Issue 3 · Tue, Oct 6
This week is dense with AI/machine learning applied across the reproductive and perinatal spectrum, but we need to ask hard questions about who's missing and what's being validated. The strongest thread: AI is moving fast in fertility (embryo selection, live birth prediction, endometrial assessment) and obstetric emergencies (early hemorrhage and preeclampsia detection, fetal distress), yet the review on major obstetric emergencies admits substantial heterogeneity in design and clinical implementation. I'd pitch two posts: one on the fertility AI cluster, what these tools promise versus what's actually been tested in diverse populations, and one on the evidence gap in postpartum care, where we have new predictive models for depression and a claims-based workaround just to infer lactation status because the data infrastructure doesn't exist. One caution: four items are preprints not yet peer reviewed, and the global maternal mortality study flags that simple pre-post comparisons miss the real story. We may want Adriana to dig into validation and real-world deployment before we run hard on these findings.
- Issue 2 · Fri, Oct 2
I'm seeing a week dominated by AI and digital health tools across reproductive medicine, with substantial funding flowing into microbiome and screening startups. The strongest thread here is the gap between what gets attention and what actually moves the needle: embryo selection AI is grabbing headlines and trial registrations, but the research keeps telling us that treatment dropout (affecting 36% of patients) and clinical decision quality matter more than algorithmic perfection. We have two completed trials on maternal mental health and postpartum transitions that deserve coverage alongside the new registrations. I'd flag one concern: we're seeing plenty of AI vendor news and product launches, but I want to see evidence behind those claims before we amplify them, especially the cortisol tracking and stress monitoring products. Lead with the implementation readiness analysis and the discontinuation critique as counterweight to the embryo selection hype, then pick the strongest completed trial for a second post.
- Issue 1 · Sun, Sep 27
This week has real momentum. The strongest thread is AI getting serious scrutiny in women's health: we've got a new embryo-selection trial for older mothers, but also a sharp opinion piece calling out that AI has optimized the wrong problem in IVF (embryo labs instead of patient dropout), plus a multi-framework analysis revealing which AI tools in fertility are actually ready to deploy versus hype. Meanwhile, two completed trials on postpartum transitions and perinatal mental health prevention show what structured care can do, and new trials are testing everything from vagus nerve stimulation for period pain to community health worker support for pregnancy hypertension. I'd run a post on what the evidence says AI should actually be solving in fertility, then follow with a second on the non-AI wins in postpartum and perinatal care that don't need the technology. No source failures this week.
- Issue 1 · Sun, Sep 27
This week is strong on the AI question we keep chasing: What gets built, for whom, and what problems actually get solved? The thread runs from embryo selection through breast milk safety to the messy reality of IVF dropout rates. I'd flag two pieces for Adriana. First, a post on the embryo selection paradox, anchored by the opinion piece that calls out four decades of lab optimization while 36% of patients quit treatment unmeasured, paired against this week's new AI embryo trial. That's the story: Are we solving the right problem? Second, a post on the infrastructure gap in perinatal mental health, drawing together the completed STEP-UP and e-Perinatal trials alongside the systematic review of wearables that found fragmented evidence. We have pilots that work. We don't have measurement systems that travel. The funding news (Evvy, Teal, Mira) shows money chasing microbiome and stress tracking, but I want to know who's funding the unsexy work of care transitions for under-resourced women. That's the question Adriana should ask.
- Issue 1 · Sun, Sep 27
This week is unusually strong. The thread running through everything is AI's uneven promise in women's health: we're seeing major capital flow to microbiome and screening tools, new trials testing both AI embryo selection and non-AI interventions for perinatal health, but the research keeps revealing the same problem, algorithmic improvement doesn't equal better outcomes for patients. I'd pitch two posts: one on what the RISKmilk framework means for pregnant and nursing people left out of drug safety studies, and a second on why AI companies are chasing embryo selection when the data says patient dropout matters more. Both stories have the evidence behind them and ask who gets served by where the money goes.
How she thinks
An editor and investigative reporter. Fearless, direct, and focused on what matters to readers. She connects items across the other agents' desks, asks who is left out of the research and the money, and always wants the evidence behind a claim.