Guide series, Part 5 of 5 · What gets measured gets managed
How would Nassim Nicholas Taleb use HbA1c and Time in Range to assess Type 1 diabetes risk?
CareLink, Clarity, LibreView and Glooko all default to a 14-day report. That default is an ensemble approach to a profoundly individual disease. In a population, the 14-day average is informative. For one person whose lifetime risk is being decided, it is roughly the precision of a horoscope.
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Population probability is not your probability
If you flip a coin 1,000 times, the population result is 50:50. If you flip the coin once, the population result tells you almost nothing about whether your single flip will be heads or tails. The two probabilities are not the same.
This distinction has a name. A process is ergodic if the time-average for one individual converges on the ensemble-average across many individuals. Coin flips are roughly ergodic over enough time. Type 1 diabetes is not.
For a person with T1D, your risk does not only average across “people like you.” It also accumulates across your own 50 to 70 years of glycaemic exposure, your own hypos, your own DKA episodes, your own cardiovascular trajectory. Population means are useful for designing trials and writing guidelines, and the average HbA1c hazard ratio (Bebu 2020) does apply to individuals on average. The error is treating the population mean as if it were your personal probability, when the cumulative-exposure path you are on is what actually delivers the outcome.
Taleb’s revolver example, and why an ensemble rate is not your rate
Nassim Taleb’s revolver example. Imagine 100 people each play one round of Russian roulette with a six-shooter holding one bullet. Roughly five out of six walk away. The ensemble survival rate is ~83%. For each individual player, that ensemble rate tells you nothing about whether they are the sixth. The two probabilities are not the same number applied to different things; they are different questions. T1D is not roulette, but the structural lesson holds: when the consequences of being on the wrong side of the distribution are catastrophic and irreversible, you have to plan for your individual path, not the population average. Taleb first used this example in Fooled by Randomness (2001) to illustrate how easily ensemble statistics mislead individuals; he returned to it in Skin in the Game (2018) to argue that this distinction is structurally invisible to people interpreting data they do not have to live with.
Most people are not “most people”
How a population splits into thirds, and why a 14-day window misleads
If a population has a mean of 70% TIR, that mean is the balance point of the distribution. It is not, despite how it is usually read, where most of the population sits; depending on the spread, very few people may sit near it at all. In any well-distributed population, roughly:
- One-third sit close to the mean (within a tight band)
- One-third sit meaningfully above (better-controlled, lower-glycator, or both)
- One-third sit meaningfully below (higher exposure, higher-glycator, or both)
If a clinic looks at your last 14-day TIR and compares it to “the consensus 70%,” they are doing two things at once. They are taking your short-term snapshot, and pretending it sits comfortably on a population mean that two-thirds of the population are not actually on. The 14-day window adds noise; the population comparison adds the wrong reference frame. Both errors point the same way: false reassurance, or false alarm.
Why short windows mislead in T1D specifically. One holiday, one virus, one stretch of shift work, one menstrual cycle, one course of steroids can move a 14-day TIR by 10 to 20 percentage points without anything changing about your underlying biology or your management. Three months smooths most of that out. Two weeks does not.
The 90-30-14 rule
Different windows answer different questions. A fortnight is weather; a season is climate. You dress for the weather and you move house for the climate, and confusing the two is how a wet week becomes an argument about global patterns. Here is what each window is for:
90 days, for risk
This is the window HbA1c itself runs on: red blood cell turnover of around 120 days, weighted to the recent weeks, which is also what the GMI formula estimates. It is the window that lets you calculate an mHGI pair (HbA1c + 90-day mean glucose), and the one that smooths out one-off events to leave your stable glycaemic exposure showing. When the question is what target makes sense for you, what therapy might shift your trajectory, or what your long-term complication risk looks like, the 90-day window is the one that holds.[1]
30 days, for patterns
This is the window for spotting structural patterns: morning highs, post-lunch crashes, weekend versus weekday differences, cycle-related shifts, exercise responses. Long enough to see the pattern repeat, short enough to be actionable. If you are tweaking basal rates, ratios, or AID settings, this is the window.
14 days, only after a change
14 days is for confirming that something specific has shifted: a new pump, a new sensor, a new insulin, a new schedule, a new medication. It is a short-term verification window, not a risk-assessment window. If a clinic uses 14 days as the default for everyone, every visit, that is an ensemble approach pretending to be a personal one.
The platform defaults are wrong by default
Look at where everyone in T1D gets their data:
| Platform | Manufacturer | Default report window | What that window is good for |
|---|---|---|---|
| CareLink | Medtronic / MiniMed | 14 days | Pattern checking after a change |
| Clarity | Dexcom | 14 days (90-day option exists) | Pattern checking after a change |
| LibreView | Abbott | 14 days | Pattern checking after a change |
| Glooko | Glooko (multi-device) | 14 days | Pattern checking after a change |
None of these defaults are wrong as such; they are calibrated for short-term clinical workflow. The problem is that the default sets the expectation. A clinician opens the report, sees a 14-day TIR number, compares it to the 70% target, and treats that comparison as a clinical signal. It isn’t. It is a noisy snapshot compared to a population statistic. Two layers of mismatch in one decision.
T1D is a Black Swan game
Taleb’s Black Swan framing applies to events that are rare, high-impact, and only obvious in retrospect. In T1D, the events that matter most are exactly this shape:
- Severe hypoglycaemia, especially nocturnal, especially driving, especially pre-DKA-rescue dosing in a stomach bug
- Diabetic ketoacidosis, especially during illness, especially during a sensor failure window, especially with new SGLT2 use
- Cardiovascular events at age 50 to 70, driven by 30 to 40 years of cumulative glycaemic exposure
- Severe non-traumatic hypoglycaemia at age 70+ as cognition begins to slow
None of these are well captured by ensemble averages. A clinic-level “average HbA1c was 58 mmol/mol last year” tells you almost nothing about who in that clinic walked into A&E with severe DKA. A clinic-level “average TIR was 65%” tells you almost nothing about who had three night-time hypos last month. The events that matter are individual, episodic, and clustered around moments of vulnerability.
Taleb’s argument in Skin in the Game (2018) is that the people interpreting the data should bear some of the consequences of being wrong. In T1D, the person bearing the lifetime cumulative-exposure consequences is the person living with it. Clinicians do carry accountability through clinical-governance routes, but they do not carry the 30-year glycaemic, hypoglycaemic and cardiovascular exposure. The structural mismatch is mostly an ergodicity problem (a 14-day snapshot is the wrong window for a non-ergodic disease) with a skin-in-the-game flavour around how target-setting decisions are made.
The implication: for individual T1D risk, do not collapse into population statistics. Use your own 90-day CGM data, your own 4 to 6 paired HbA1c values collected over at least 9 months, your own glycator status, your own CGM zone. Part 4’s personalised target is one of the few clinical tools that does this; almost everything else available right now defaults back to ensemble.
What this changes for how you use your CGM data
If you are the person with T1D
- Open the 90-day view in your CGM app at least once per quarter. Note your mean glucose and your TIR.
- Pair it with your most recent lab HbA1c from the same window. That is one mHGI data point.
- Four to six of these pairs, spread across at least 9 months, is enough to know your glycator status.
- Compare your 90-day TIR against your personalised target from Part 4, not against the consensus 70%.
If you are a clinician
- Default the person’s CGM platform view to 90 days for risk reviews. Use 30 days for the pattern conversation. Reach for 14 days only when there has been a recent change worth verifying.
- Do not interpret a 14-day TIR shift as a clinical change unless you have ruled out a 14-day-window event (illness, holiday, cycle, steroids).
- For complications-risk discussions, anchor on HbA1c and 90-day mean glucose together, not on TIR alone (see Part 1 on why HbA1c is still indispensable).
- If you are pooling CGM data across people with different sensors, the comparability problem (Part 3) compounds with the ergodicity problem. Both layers of mismatch apply.
The Via Negativa thread through this guide
The four Taleb books, and where each one lands in this guide
Taleb’s four books form a spine that runs through everything GNL does. This guide is one expression of it.
- Fooled by Randomness (Part 5): a 14-day TIR snapshot fools you exactly the way a single cricket innings or a single stock return fools you. Use 90 days for signal.
- The Black Swan (Part 5): severe hypos, DKA, and late-life cardiovascular events are rare, high-impact, and clustered around individual vulnerability. Ensemble averages do not protect you from your own tail.
- Antifragile (Part 4): antifragility is the property of getting stronger from variability and stress, the opposite of fragility (which gets weaker). A 3 mmol/mol HbA1c gap, which is where GNL’s glycator boundary sits (Grade E modelling, with the reasoning set out in Part 4), compounds over a T1D lifetime the way a 1% savings gap compounds over a working life: small, persistent exposures define the trajectory. Removing them (Via Negativa, the subtraction move) matters more than adding interventions on top.
- Skin in the Game (Parts 4 and 5): the person interpreting your data should bear some of the consequence of being wrong. In T1D, that person is you. Your target should be set from your own biology (glycator status), your own device (CGM zone), and your own 90-day trajectory; not from a population statistic produced by people who will not carry the result.
The Via Negativa principle ties it together: what you remove matters more than what you add. Remove the 14-day default. Remove the assumption that everyone glycates the same. Remove the non-inferiority margin that flatters the headline. What remains is your individual signal, and that is what this guide exists to find. The same philosophy drives Via Negativa Health, the consultancy arm of GNL: subtraction over addition, skin in the game, and building things that get stronger from use.
Notes
Note [1], collected from the section above
- Four to six pairs of (HbA1c + 90-day CGM mean glucose), collected over at least 9 months, is what the calculator on gnl-grace needs to estimate your mHGI properly (Part 2). This is not because you cannot calculate mHGI from one window; it is because your individual probability stabilises with more independent observations of your own biology. ← back to text
Part 5 of 5
The 90-Day Rule and Ergodicity