How it works
Adaptive spaced repetition
OphthoMentor doesn't just serve questions — it schedules them. Every question becomes a memory card timed to resurface right before you'd forget it. Follow the five steps below and you'll see the whole idea, start to finish.
In one line: we model how strong each memory is, watch how you answer, and show every question again at the last useful moment — spending your minutes on your weakest topics.
You forget — and it's predictable
Memory fades along a known forgetting curve. Cram once and most of it is gone in days. But review an item just as it starts to fade and the curve resets a little flatter each time — so the same fact needs fewer and fewer reviews to stick. That's the spacing effect, one of the most replicated findings in learning science.
Dashed: recall without review. Solid: each spaced review lifts and flattens the curve.
The catch: to hit that perfect moment, we have to know how strong each memory is. So we model it.
We model your memory with three numbers
Instead of a fixed ladder of intervals, every card tracks three live values. The middle one — Stability — is the one that matters most.
Difficulty (1–10)
How hard this item is for you. Harder items grow more slowly and come back sooner.
Stability (days)
The key number: how many days your memory lasts before recall fades. Every good answer makes it grow, so the gaps get longer.
Retrievability (0–100%)
Your chance of recalling it right now. It drops as time passes — we review you just before it gets too low.
Retrievability falls smoothly from your last review — R(t) = (1 + 0.2346 · t / S)-0.5 — where t is days elapsed and S is stability. Bigger stability, slower fade, longer gap. This is the DSR model behind the open-source FSRS scheduler (the same modern engine used by up-to-date Anki).
Every answer updates the model
Answering isn't just a score — it re-estimates your stability and difficulty, which resets the next due date. Recall it well and the gap stretches; miss it and it shrinks so you see it again soon.
Your confidence does the grading
In most apps you rate each card yourself. Here you don't: we read three signals — right or wrong, how confident you said you were, and how fast you answered — and turn them into the grade. A lucky guess barely raises stability and returns soon; a fast, confident, correct answer means the memory is strong, so stability jumps and the card waits far longer. (In Review you can still grade by hand with Again / Hard / Good / Easy.)
It aims where it matters most
Two dials make the schedule yours:
Your target retention
Pick how well you want to remember — 70–97% (default 90%). Higher means more frequent reviews and firmer recall; lower means fewer reviews and more new material.
Weakest-subspecialty weighting
The queue leans toward the subspecialties you score lowest in, so scarce study minutes go where they move your board readiness the most — not just to whatever is due.
Put together: a proven memory model (steps 1–2), fed by how confidently you answer (steps 3–4), pointed at your weak spots (step 5). That combination — not the algorithm alone — is what makes it adaptive.
This is one half of the system. The other is how we measure your exam readiness — turning your answers into a single, honest score. And it all sits on top of clinician-written, reviewed questions. See it all on the how-it-works overview.
The science behind it
The method rests on decades of cognitive-science research and a modern, open scheduling algorithm:
- Memory: A Contribution to Experimental Psychology
Ebbinghaus, H. (1885/1913) — The original forgetting curve — recall decays predictably over time.
- Distributed practice in verbal recall tasks: A review and quantitative synthesis
Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006) — Psychological Bulletin — meta-analysis establishing the spacing effect.
- Test-enhanced learning: Taking memory tests improves long-term retention
Roediger, H. L., & Karpicke, J. D. (2006) — Psychological Science — the testing (retrieval-practice) effect.
- Making things hard on yourself, but in a good way: Creating desirable difficulties
Bjork, E. L., & Bjork, R. A. (2011) — UCLA Bjork Learning & Forgetting Lab — why effortful recall builds durable memory.
- Spaced repetition promotes efficient and effective learning
Kang, S. H. K. (2016) — Policy Insights from the Behavioral and Brain Sciences — instructional implications.
- A stochastic shortest path algorithm for optimizing spaced repetition scheduling
Ye, J., Su, J., & Cao, Y. (MaiMemo) (2022) — ACM SIGKDD — the research behind FSRS, the scheduler OphthoMentor runs.
- Free Spaced Repetition Scheduler (FSRS)
Open Spaced Repetition — Open-source reference implementation of the DSR memory model.