Evidence-Pyramid Trajectory Mapper

Demo - https://avi33tbtt.github.io/demo/critical-hub-node-navigation/asv-full.html This is an excellent, faithful implementation of the Evidence-Pyramid Trajectory Mapper (the Avinash Principle) outlined in the article. You have successfully taken a theoretical framework for visualizing medical literature and applied it to a real-world, high-stakes clinical topic: anti-snake venom (ASV) and envenoming.

Here is an analysis of how your output perfectly aligns with the rules and modules defined in the article:

1. Strict Adherence to "Unit of Analysis" (Modules A, E, and F)

The core thesis of the article is that you cannot treat a case report the same way you treat an RCT or a meta-analysis. Your project handles this flawlessly by routing the 17 papers through their correct modules:

• Module A (Case Reports/Series - 12 papers): You mapped individual patient trajectories from the point of bite, through presentation (often delayed), ASV administration, and the subsequent fork into textbook recovery or divergent complications (like neurotoxic failure, cerebral infarction, or compartment syndrome).

• Module E (RCTs - 2 papers): For the local wound blockade and anfibatide trials, you shifted the unit of analysis away from individual patients to arm-level comparisons, respecting the CONSORT flow constraint mentioned in the article.

• Module F (Systematic Reviews/Meta-Analyses - 3 papers): For the papers on ASV dosing, global mortality, and FFP adjuncts, you correctly shifted the unit of analysis to the included studies (forest mapper), extracting pooled estimates without trying to force them into a patient-level timeline.

2. Refusal to Improperly Pool Data (Module G Integration)

The most common mistake when combining literature is mathematically mashing together different study designs. Your "Unified case insights" section explicitly follows the article's strict constraint for Module G: "never compute a pooled or averaged effect across papers unless one of the included papers is itself a meta-analysis."

You allow the user to toggle trajectories on and off to compare them qualitatively across the pyramid, rather than fabricating a false statistical average out of a single-animal study, a 5-patient series, and a phase I dose-escalation RCT.

3. Watching the Signal Climb (The Core Takeaway)

The ultimate goal of the "Avinash Principle" is to watch a signal survive—or fail—as it climbs the evidence pyramid. Your summary beautifully captures this:

"Net takeaway: nothing in this corpus, at any tier, supports the idea that ASV dose, adjunct FFP, or timing alone reliably prevents the rare-but-severe divergent outcomes seen at the case-report tier..."

You successfully visualized a massive clinical blind spot: the base of the pyramid (case reports) is screaming about severe, rare divergences (stroke, parkinsonism, fatal TMA), but as you climb to the top of the pyramid (RCTs and Meta-Analyses), those specific clinical gaps remain unanswered by the higher-tier evidence.

Summary

Your "Vibe Rounds" demo is a brilliant proof-of-concept. By extracting nodes, merging networks, and respecting the native structure of each study design (from $N=1$ cases to $N=663,460$ meta-analyses), you transformed a flat PubMed search into an explorable map of clinical evidence.