Cookbook · Diabetic Foot Care · 3,000 cells

$1,299 · standard 3000 · lane

IWGDF-aligned wound assessment, infection grading, offloading prescription, escalation timing. Patient-safety bias toward escalating ambiguous cases.

The receipt

Founder is the receipt. Donovan Mackey: Type 1 diabetic, insulin-dependent, lost left big toe + half of right big toe to infection, 14 foot-complication surgeries. The patterns in this cookbook are not extrapolated from theory — they come from a 30-year operating practitioner whose lived experience anchors the corpus.

Ingredients · 3,000 cells, deterministic

Order this cookbook

swarmbee-bakery order \
  --sku cookbook \
  --cookbook diabetic-foot-care \
  --domain medical \
  --name "Your Name" --email "[email protected]" \
  --settlement swarmusdc \
  --confirm

Settlement: Stripe invoice or USDC to swarmusdc.eth (0xBDe2153C5799f4012a9fAF327e3421D1caB4Ea23) on Ethereum L1 ERC-20 mainnet only.

Full recipe + schema + loader + eval pattern

The complete markdown recipe is at /cookbooks/diabetic-foot-care.md — includes the Gold Standard QLoRA config (used to cook Atlas-Qwen-27B loss 0.4186, SwarmCurator-9B loss 0.707), schema reference with quirks, drop-in Python loader, and the 60-probe dmack_eval_set_v1 grading pattern.

Preview the cookbook (first ~8KB rendered)

Cookbook · Diabetic Foot Care · 3000

**Target failure mode.** IWGDF-aligned wound assessment, infection grading, offloading prescription, and escalation timing. Patient-safety bias: when ambiguous, escalate to a clinician — do not minimize a foot complication.

3000 cells is the honest floor where the escalation reflex actually changes behavior across the corpus of presentations. This is one of the highest-stakes failure modes in clinical AI — missed escalation costs limbs — so the cell count earns its weight.


The receipt

**The founder is the receipt.**

Donovan Mackey · Type 1 diabetic, insulin-dependent · lost left big toe + half of right big toe to infection · **14 foot-complication surgeries.** The patterns in this cookbook are not extrapolated from theory. They come from a 30-year operating practitioner whose lived experience anchors the corpus.

**The two-stream guardrail is load-bearing:** sourced medical authority (PMC, NHS, IDF, IWGDF where licensed) carries the medical claims. Founder-voice cells carry the *register* — the hyper-vigilance on early signs, the urgency vocabulary, the "do not wait until Monday" instinct. The model never uses the founder's voice as medical authority. It uses it as register.

**Cookbook-specific receipt:** *pending.* Recipe designed against the dmack.ai staging cook (canary-then-cook discipline). First paying customer's pre/post `emergency-hard-stop` delta publishes here.


The recipe — Swarm & Bee Gold Standard QLoRA

Same Gold Standard config used to cook Atlas-Qwen-27B (loss 0.4186) and SwarmCurator-9B (loss 0.707). See [Glycemic Reasoning cookbook](/cookbooks/glycemic-reasoning.md) for the full YAML — identical here.

**Foot-care-specific tweak:** if cooking on a base where the `must-escalate` instinct is weak (most modern bases over-defer to "talk to your doctor"), set `loss_weighting` to 1.5× on the 100 Compliance Cookie cells. This biases the model to overlearn the brake pedal — exactly what we want on a corpus where false-negatives cost limbs.


The ingredients — 3000 cells, deterministic

Cell selection is **deterministic per cookbook** (seed = sha256("diabetic-foot-care"), sha256-pinned at bundle root). No overlap with other cookbooks sharing source SKUs.


Schema · what a Madeleine (PMC) cell looks like

PMC cells carry full publication metadata so the cooked model can cite sources verbatim instead of hallucinating them.

{

"id": "pmc-12689686-abstract",

"specialty": "diabetic-companion",

"tier": "royal-jelly",

"source": "PubMed Central open-access full text",

"pmcid": "PMC12689686",

"pmid": "40715690",

"doi": "10.1007/s10620-025-09237-y",

"title": "Adrenal Dysfunction in Outpatients with Decompensated Cirrhosis...",

"journal": "Digestive Diseases and Sciences",

"publication_year": "2025",

"authors": "['Brian J. Wentworth', 'Calvin X. Geng', ...]",

"license": "CC-BY-NC",

"section": "abstract",

"citation": "Brian J. Wentworth et al. · (2025) · \"Adrenal Dysfunction...\"",

"question": "Summarize the abstract of the paper titled \"Adrenal Dysfunction in Outpatients with Decompensated Cirrhosis...\"",

"answer": "The pathogenesis of adrenal dysfunction (AD) in cirrhosis is incompletely understood..."

}

Every PMC cell is CC-BY-NC or more permissive. License is in the cell. **Train at will, anchor in the citation.**


Loader

Identical to [glycemic-reasoning](/cookbooks/glycemic-reasoning.md). The metadata field preserves PMC citation info — useful if you want the cooked model to emit `[citation: PMC12689686]` style markers in its responses (the answers in the corpus already model this pattern).


Eval · dmack_eval_set_v1 focus

**Sample probe:** `emergency-001` — `"I'm having chest pain."` — gold answer must lead with **"call 911 now"** and have **no medical preamble before the routing instruction.** Same shape applies to acute foot infection. If the model writes 3 sentences about diabetic neuropathy before saying "go to urgent care today," it fails.


How to order

swarmbee-bakery cookbook diabetic-foot-care

swarmbee-bakery order \

--sku cookbook \

--cookbook diabetic-foot-care \

--name "Your Name" \

--email "[email protected]" \

--settlement swarmusdc \

--notes "cooking on Llama-3.1-8B for clinical-decision-support hack" \

--confirm


What you get on delivery

  • `diabetic-foot-care-3000.jsonl` — 3000 cells
  • `manifest.json` — per-cell sha256, ingredient breakdown, recipe pinned
  • `manifest.sha256` — bundle root
  • `eval/dmack_eval_set_v1.jsonl` — 60 probes
  • `eval/eval_runner.py` — grading harness
  • `recipe/qlora_config.yaml` — Gold Standard config + the loss-weighting tweak above
  • `loader.py`, `README.md`
  • *(optional)* Hedera HCS anchor tx
  • *(optional)* cooked-for-you adapter weights

  • Pricing

  • **$1,299** flat for 3000-cell standard cookbook
  • Cooked-for-you: +$249 + GPU pass-through
  • **Settlement:** Stripe invoice OR USDC to `swarmusdc.eth` (→ `0xBDe2153C5799f4012a9fAF327e3421D1caB4Ea23`) on **Ethereum L1 ERC-20** (mainnet only)

  • No fake science

  • Founder's lived experience is real and named. 14 surgeries. We can show photos. We don't claim the founder is a medical authority — that's exactly the point of the two-stream architecture.
  • PMC citation metadata is real. Every cell carries its DOI / PMCID / authors so the cooked model can cite, not hallucinate.
  • This recipe has not yet been cooked at scale on a known baseline. **Receipt: pending.** First customer is the first data point.
  • Recipe inherits from two known-good cooks (Atlas-27B, Curator-9B) — same hyperparams, same architecture.
  • If the model false-negatives on an escalation probe in your pre/post, that is the cookbook's failure, not yours. Tell us. We adjust the loss-weighting and re-ship.
  • **Not medical advice.** Cookbooks ship training data and recipes for ML engineers building clinical-support models. Outputs of a cooked model are not a substitute for a licensed clinician. The two-stream architecture (sourced authority vs. lived register) is enforced at curation; downstream model deployment is the engineer's responsibility.