How GNL Works

What this page covers

• Disclaimer and framing — the 20/80 educational philosophy that sits beneath all GNL output
• GNL evidence base — the guidelines, consensus papers, and original research that underpin every tool
• GNL Grace — how the AI educational advisor was built, by whom, and what powers it
• Evidence applied — how guidelines and real-world data were used to validate every algorithm assumption
• Six explorer walkthroughs — the clinical question each tool answers, its algorithm basis, and what real-world testing found
• GNL Podcast — how the podcast series layers into and extends the evidence base
• Contact — where to direct questions about each area

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What this means

GNL educational tools give you the 20% of knowledge — with the right mindset and framework — that gets you 80% of the way there. This is not a limitation to apologise for. It is the honest account of what any educational resource, however well-built, can deliver.

Research across complex domains consistently shows that a small fraction of the right knowledge, applied with the right framework, produces the majority of the benefit. In T1D education, this holds true — and GNL’s tools are designed around it.

What the remaining 20% requires

• Your individual data — your physiological responses to insulin, food, activity, sleep, and stress, which no population-average tool can know
• Self-discovery over time — the iterative process of observing what works for you specifically and building your own pattern recognition
• A personalised human network — clinicians, educators, and peers who know you, your history, and your context in ways that cannot be encoded in an algorithm

How GNL is designed around this

Every GNL Explorer, guide, and Grace response is designed to show what the evidence says happens on average for a person with your characteristics — and then explicitly hand you back to your own context and your care team. GNL does not guess where it does not know. It does not extend population findings to individual prescriptions. And it does not pretend that 80% of the way there is the same as arriving.

GNL’s knowledge base is built on three layers: international clinical guidelines, expert consensus papers, and a validated real-world dataset accessed through academic research collaboration. All claims carry an evidence grade.

International guidelines — Grade A

Key consensus papers

GNL original research

Real-world validation — Cockpit 1.0/daily dataset

Published research using the Diabetes Cockpit

The following peer-reviewed conference abstracts have used this dataset. Two involve GNL’s own Scientific Directors.

Dataset: Cockpit 1.0/daily dataset, Syno by Syntactiq Dynamics FlexCo (syntactiq.ai). Contact: contact@syntactiq.ai.

GNL Grace is the world’s first T1D educational AI advisor built on a curated, evidence-graded clinical knowledge base. Here is the full account of how it was built, what powers it, and who built it.

The team

What powers Grace

Grace is powered by Claude by Anthropic — one of the most capable large language models available. Claude provides the natural language understanding that allows Grace to interpret any T1D question and generate a coherent, evidence-grounded response. The value of this approach is not the AI itself — it is the combination of a state-of-the-art language model with GNL’s curated clinical knowledge base. Neither element works without the other.

The language model generates responses. GNL’s curated knowledge base determines what those responses are grounded in. The clinical framing — what Grace can say, how it is qualified, and when it refers users back to their care team — is built into the knowledge base itself, not left to the AI to decide.

How a Grace query works

• 1
  Question received — via the GNL website widget or the authenticated API endpoint for partners.
• 2
  Knowledge base searched — Grace searches GNL’s curated clinical evidence across all topic domains. The search is semantic — it finds relevant content even when the exact words do not match the question.
• 3
  Evidence-graded response constructed — retrieved content is used to build a response that cites the evidence grade for every claim (A/B/C/D). Where evidence is absent or weak, Grace says so explicitly.
• 4
  Signposting — where a GNL Explorer, guide, or podcast episode addresses the question in depth, Grace points to it. Where the question requires individual clinical judgment, Grace says so and refers the user to their care team.
• 5
  Disclaimer applied — every response carries the population-average educational framing. Grace never presents an output as individual clinical advice.

What Grace can and cannot do

API access

Grace is available via authenticated API for clinical and commercial partners who want to embed the best T1D educational advisor in their platforms. The full knowledge base, clinical framing, and evidence grading are all delivered at API level — no degraded copies, no stripped disclaimers. Enquiries: john@theglucoseneverlies.com.

GNL ran a systematic real-world validation programme across all six explorers, testing every algorithm assumption against a validated longitudinal T1D dataset. Here is how the process worked and what it found.

The validation process — five stages

• 1
  Algorithm design from published evidence — every assumption documented with source citation and evidence grade. Minimum Grade C to enter production. Grade A/B required for any numerical output.
• 2
  20+ years educator experience as context — clinical evidence sets the numbers. Educator experience determines which questions matter most in practice and how outputs should be communicated to avoid common misapplications.
• 3
  Real-world validation — 30+ structured queries — every major assumption tested against more than 1.5 million patient-days (Cockpit 1.0/daily dataset, Syno by Syntactiq Dynamics FlexCo, syntactiq.ai). Where assumptions are confirmed, they are retained. Where challenged, the algorithm or framing is updated. Where intraday data is needed, the limitation is documented explicitly.
• 4
  Piloting with GNL subscribers — structured usability and comprehension testing with an engaged T1D community before broader release. Framing revised where outputs are consistently misunderstood.
• 5
  Continuous iteration — scholar alert pipeline tracks new publications daily. Algorithm assumptions reviewed as evidence evolves. Every change logged in the GNL Compliance Dossier.

Eight counter-intuitive findings — Via Negativa

These are the findings where real-world data contradicted what guidelines and clinical intuition predict. Each has resulted in an algorithm update or reframing.

Explorer 1 of 6

Activity and Exercise Explorer

The clinical question this tool answers: what is exercise actually doing to my glucose, and how can I plan around it safely?

Algorithm basis

Each exercise type is associated with a validated glucose drop rate per 20-minute block, derived from the clinical literature. Aerobic exercise carries the highest per-unit drop rate; resistance and anaerobic exercise carry a lower rate due to counter-regulatory catecholamine release. The algorithm weights current glucose, CGM trend, insulin activity, and exercise duration to produce a risk classification and directional estimate — not a precise prediction.

Key sources: Riddell et al. 2017 (Lancet Diabetes Endocrinol, PMID: 28126459); ISPAD 2022 exercise chapter (Adolfsson et al.); T1DEXI study; Moser/EASD 2025 AID consensus; Pemberton et al. 2023 post-meal activity study.

What real-world validation found

Explorer 2 of 6

Exercise IOB Calculator

The clinical question: how much insulin is still active, and what does that mean for my safety going into exercise?

Algorithm basis

The calculator uses a pharmacokinetic model of rapid-acting insulin decay to estimate the fraction of the bolus still active at the point of exercise. This produces a risk band (Low / Moderate / High / Very High) based on estimated active insulin relative to body weight. Carbohydrate and insulin adjustment recommendations are derived from the risk band combined with exercise type and glucose trend.

Key sources: Heise et al. 2017 (rapid-acting insulin pharmacokinetics — IOB decay model); ISPAD 2022 carbohydrate recommendation tables; Riddell et al. 2017 insulin adjustment percentages.

What real-world validation found

Explorer 3 of 6

AID System Explorer

The clinical question: I am considering or starting an AID system — what can I realistically expect?

Algorithm basis

The algorithm maps baseline TIR against therapy type to model a realistic benefit range. Outputs distinguish between the clinical trial benefit range and the real-world observed advantage. The strongest confirmed benefit — overnight multi-day cycle-breaking — is explicitly highlighted. The algorithm does not overstate the benefit of AID for highly engaged MDI users, where the real-world gap is substantially smaller.

Key sources: Pivotal AID trials (Brown et al., Beck et al.); real-world registries (Choudhary, Forlenza); ISPAD 2024 diabetes technologies chapter.

What real-world validation found

Explorer 4 of 6

Hypo and Hyper Explorer

The clinical question: my glucose is going low or high — what is actually happening, and how do I think about the pattern?

Algorithm basis

A five-tier glucose classification framework maps current glucose to clinical categories (severe hypo / mild hypo / in range / mild hyper / severe hyper), each with a mechanism explanation and contextual guidance. The correction factor tool uses a TDD-based insulin sensitivity factor formula, validated as directionally accurate in real-world data. The hypo treatment pathway is grounded in the ISPAD/ADA 15g consensus, with explicit over-correction warning based on the next-day TBR finding.

Key sources: ISPAD 2022/2024 glucose thresholds; ADA 2026 correction factor framework; 15g hypoglycaemia treatment consensus.

What real-world validation found

Explorer 5 of 6

Exercise Planning Explorer

The clinical question: I am planning a session — what should I think about before, during, and after?

Algorithm basis

A before-during-after framework structured around device type. MDI users receive insulin reduction percentages by exercise type and duration. AID users receive a universal pre-exercise bolus reduction guidance informed by the ISPAD/EASD consensus, with overnight AID management framed separately (no insulin reduction offered — AID automode handles overnight basal). Pre-exercise glucose target ranges are derived directly from the ISPAD 2022 Table 2 targets.

Key sources: ISPAD 2022 exercise chapter (Adolfsson); Riddell et al. 2017; Moser EASD/ISPAD 2025; Campbell et al. 2015 (overnight carbohydrate protocol); Rabasa-Lhoret et al. 2001 (aerobic dose reduction).

What real-world validation found

Explorer 6 of 6

Alcohol and T1D Explorer

The clinical question: what does alcohol actually do to glucose management, and how do I think about the overnight risk?

Algorithm basis

The explorer is built around the hepatic gluconeogenesis inhibition mechanism — alcohol blocks the liver’s ability to release stored glucose in response to falling blood sugar, shifting the overnight hypo risk window to 6-24 hours post-drinking. AID systems are explicitly framed as reactive, not proactive: they can respond to falling glucose via automated suspension, but they cannot anticipate the metabolic effect of alcohol and are not a safety net for alcohol-related overnight hypo.

Key sources: Hepatic gluconeogenesis inhibition literature; ADA Standards; ISPAD. Plus 10 specialist references (GNL Compliance Dossier Appendix B).

What real-world validation found

The GNL podcast is not a content product that sits alongside the evidence base. It is a direct input into it. Every episode adds verified clinical evidence to GNL’s curated knowledge base, and Grace can point to specific conversations when they represent the deepest available discussion of a topic.

How the podcast layers into the evidence base

• 1
  Each episode involves a leading T1D clinician or researcher — including Professor Othmar Moser (Scientific Director, GNL), Professor Dessi Zaharieva (Scientific Director, GNL), and a growing roster of international diabetes specialists.
• 2
  Episode content is reviewed for clinical evidence — key claims are checked against published literature, graded, and where new or distinct from existing evidence, added to GNL’s curated knowledge base as a source summary.
• 3
  Cross-referenced by clinical topic — Grace can identify the specific episode that provides the most useful in-depth discussion on any topic covered by the podcast library, and direct users to it by name.
• 4
  The CGM series — a structured systematic evidence series applied to CGM device discussion, with study design scoring methodology applied to each device. Grace uses this series to inform CGM selection responses — pointing to the top three clinical decision factors grounded in the evidence.

Different questions go to different people. Here is where to direct enquiries depending on the area.

GNL Grace and Explorers

Questions about how Grace works, the evidence behind any explorer, partnership and API access enquiries, and anything related to the GNL educational platform.

GNL Podcast

Guest proposals, production queries, episode feedback, and all podcast-related enquiries.

Research and clinical partnerships

Manufacturer partnerships, academic research collaboration, clinical platform integration, and Via Negativa educational consultancy enquiries.