Submission-readiness gateway
Inspect the two scientific tracks, current research-release metrics, provenance and the gates still needed before formal submission.
Scientific review dossier →
Health evidence · research infrastructure · public observatory
SCC Vaping
An independent, evidence-led research and public-information project designed to continuously collect, cross-reference, test and communicate evidence relating to vaping.
SCC Vaping is the active project identity. The former internal name Vaping26 remains in some filenames, workflow names and data contracts for compatibility and historical provenance, but it no longer defines the project or imposes an artificial 2026 end date.
An SCC Nexus research programme. Evidence before conclusion; independent scrutiny and human scientific review remain essential.
Publication, external review and partnerships
Three accountable routes to inspect the project.
These publication-safe entry points separate the scientific methods proposition from commercial due diligence and restricted evidence. No page claims independent scientific approval or offers access to confidential review material.
Responsible due diligence
Capabilities, collaboration boundaries, rights, independent research safeguards and non-asserted commercial financials.
Business scrutiny dossier →Redacted public summaries
Which evidence classes are public, withheld or restricted, and why genuine access controls are maintained elsewhere.
Public redaction register →Current academic status: methods/infrastructure is an external-review candidate, not yet submission-ready; substantive health-evidence synthesis is not yet submission-ready. Automated evidence counts are not conclusions.
Official short synopsis
Evidence before conclusion.
SCC Vaping is an independent, continuously updated evidence observatory that combines deterministic data engineering, systematic evidence surveillance, human review and carefully bounded machine learning to collect, cross-reference and audit research relating to vaping. Its private research engine preserves provenance and applies a deny-by-default publication firewall; a separate public observatory communicates only validated, publication-safe material. The project is also testing whether modern ML and source-grounded AI can reduce evidence-review workload while maintaining high recall, transparency, reproducibility and human scientific accountability. Its purpose is not to advocate a predetermined position on vaping, but to make the evidence base easier to interrogate, verify, challenge and update.
Live verified public release
Current evidence-system status
This panel mirrors publication-safe aggregate metadata from the canonical SCC Vaping Observatory. It does not expose raw research records and it does not convert coverage counts into health conclusions.
6 October 2026
Latest validated public release mirrored from the SCC Vaping Observatory.
11 / 43
Successful source pulls in the latest published run against the registered source set.
910 records
Quality-gated candidate literature records after filtering, quarantine and deduplication.
0 reviewed · 0 synthesis-ready
Conclusion-sensitive synthesis remains blocked until source-grounded human scientific review is completed.
Publication contract: deny-by-default-v2. No source failures were reported in the latest published run. Inspect the SCC Nexus status mirror →
Principal aim
Not to prove that vaping is good or bad.
The central aim is to build an auditable system capable of asking increasingly precise questions and showing what the available evidence can — and cannot — support.
- Continuously identify relevant studies, trials and official evidence.
- Detect missing, duplicated, corrected or retracted evidence.
- Keep smoking-cessation evidence separate from never-smoker, youth and exposure questions.
- Retain provenance down to source records where possible.
- Test competing explanations and expose uncertainty or disagreement.
- Publish only material that passes validation and publication controls.
- Test whether modern automation, ML and AI can make evidence surveillance substantially faster without making it less trustworthy.
Three deliberately separated layers
Research machinery, public communication and academic collaboration.
sccvaping/research
The scientific machinery. Its pipeline is register → collect → snapshot → validate → cross-reference → normalise → audit → aggregate → publication firewall.
Current sources include PubMed, Europe PMC, ClinicalTrials.gov, Crossref/Retraction Watch, MHRA, Companies House discovery, ONS/NHS prevalence data, OHID Fingertips and optional OpenAlex enrichment. Raw and investigative material remains private; public release is deny-by-default.
ML may prioritise review, detect anomalies or assist bounded tasks, but may not independently establish causation, illegality, evidence grades or synthesis eligibility. AI extraction must remain source-grounded, provenance-preserving and human-reviewed.
SCC Vaping Observatory
The public repository contains a deployed branded evidence website covering evidence, health, cessation, young people, prevalence, methodology, review and academic collaboration, together with public-safe research-status data, evidence summaries and a regulatory timeline.
The next step is to evolve this from a conventional website into an interactive evidence observatory with searchable studies, filters, timelines, trial/publication relationships, provenance drill-down and downloadable public-safe datasets.
Open the public observatory ↗Google Drive collaboration layer
A separate academic workspace provides curated sharing areas for Studies and Evidence, Reports, PDF and Paper Library, Methods and Protocols, Data and Exports, Presentations and Correspondence, and Archive.
This provides an appropriate collaboration surface for academics who need more than the public website without exposing the private working engine.
Evidence status
Provenance · integrity · verification · validity
Provenance
Evidence intake distinguishes study records, protocols and post-publication notices, retaining source and study-design context before automated triage.
Integrity
Deterministic processing, source-health auditing and a deny-by-default publication firewall protect the public layer. Ambiguous evidence is allowed to abstain rather than being forced into a confident label.
Verification
Automated harvesting, validation, evidence-card infrastructure, registered-trial/publication linking, prevalence processing, research-integrity monitoring and human-review infrastructure are now implemented within the research system.
Validity
The system is designed for evidence discovery, surveillance, triage, structured extraction and review support. Automation is not equivalent to scientific quality appraisal, causal inference or clinical guidance.
Current project synopsis: 4 October 2026 · live release metadata is mirrored separately above
Achievements so far
Beyond a prototype.
The project now has enough independent infrastructure to operate as a coherent evidence system rather than a loose collection of experiments.
Mature private research repository
Dedicated sccvaping account structure, automated harvesting and validation, evidence-card and synthesis infrastructure, registered-trial/publication linking, prevalence processing and source-health auditing.
Deny-by-default firewall
Public-safe outputs are separated from raw and investigative material, with provenance-preserving publication controls and independent Drive backup credentials.
Bounded ML and AI governance
ML review prioritisation, human-review infrastructure and formal AI/ML rules keep assistance distinct from scientific judgement.
Academic sharing structure
A curated Google Drive workspace now supports academic evidence packs, methods, exports, reports, correspondence and archive material without exposing the private engine.
Innovation lifecycle
Promote what survives evaluation, not what merely looks impressive.
A new method graduates only if it outperforms an appropriate simpler baseline without damaging provenance, reproducibility or human accountability.
DiscoverHypothesisBaselineLocked evaluationSandboxEvaluateHuman benchmarkAuditPromote / rejectMonitorRevalidate
This governance has already proved useful: an invalid experimental status was caught during the first implementation, corrected, and the subsequent main validation completed successfully.
Current innovation programme
Six concrete experiments.
AI belongs here as a tool for finding, ranking, checking, comparing and assisting — not quietly becoming the scientist.
Active-learning screening
Test whether intelligent prioritisation can reduce manual screening workload while maintaining recall.
LLM-assisted second extraction
Benchmark source-grounded extraction assistance against human-reviewed reference material.
Trial/publication graph ranking
Use relationships between trials, publications, DOIs, authors and institutions to improve evidence discovery and prioritisation.
Evidence-drift monitoring
Detect material changes, corrections, retractions and shifts in the evidence base over time.
Citation-grounded academic RAG
Investigate a retrieval system that answers only from traceable project evidence and preserves citation context.
Selective / conformal classification
Explore calibrated abstention only once sufficient independent human labels exist.
Next-stage gates
Move from strong infrastructure to reviewable science without skipping the human-review boundary.
- Complete the first governed human evidence-review pilot on a predeclared synthesis question.
- Constitute independent VVIP scientific review with declarations and traceable reviewer dispositions.
- Freeze and reproduce a methods/infrastructure release candidate before formal submission.
- Replace the remaining legacy transport-relay dependency with an SCC Vaping-controlled publication path of equal or stronger assurance.
- Enforce protected-change controls for authoritative production branches or document and test an equivalent control.
- Continue building the public study/evidence explorer, trial-publication graph and evidence-drift monitoring without weakening provenance.
- Benchmark active learning and source-grounded AI assistance against human-reviewed reference material before promotion.
- Keep substantive health, cessation and effect synthesis blocked until comparator, outcome, effect-estimate and risk-of-bias fields are source-grounded and reviewed.
Submission readiness
Two tracks, deliberately kept separate.Track A — methods/infrastructure: the research engine currently records the project as an external-review candidate, not yet submission-ready. The architecture, deny-by-default publication firewall, provenance, VVIP governance materials, successful Google Drive backup and restore proof, and reproducibility tooling are substantial foundations, but independent review, frozen release-candidate review, protected-change controls and retirement of the legacy relay remain material gates.
Track B — substantive evidence synthesis: not submission-ready. The latest published readiness report records 916 candidate evidence cards, zero completed human-reviewed records and zero conclusion-sensitive synthesis-ready records. That is an intentional scientific safeguard, not a missing website statistic.
The review pack is therefore designed to make scrutiny easier without overstating readiness: reviewers should be able to trace the source set, publication manifest, automated boundaries, limitations and unresolved gates before any conclusion-sensitive manuscript is advanced.
Public boundary
Transparency without exposing operational secrets.
Public pages describe purpose, evidence principles, methods, validated status and intended outputs. Credentials, private working data, restricted source material, anti-abuse controls and security-sensitive implementation details remain outside the public surface.
The project does not claim that automated discovery, classification or extraction is scientific verification. Human scientific accountability remains explicit wherever conclusion-sensitive evidence is concerned.
SCC Vaping
Evidence that can be interrogated, verified, challenged and updated.
The project is designed to improve the speed of evidence surveillance without trading away provenance, uncertainty, reproducibility or human responsibility.
Open the SCC Vaping Observatory