Templates for CLAUDE.md, IMPLEMENTATION_PLAN.md, and the 3 standard tables. v3 updates: Stage 4 (Methods) uses 3-axis structure, not flat 10-subsection list. Bibliography target removed.
# [Topic] Literature Review Writing Guidelines
## Project info
- **Topic**: [specific topic]
- **Target journals**: [Tier 1: e.g., Nat Rev Cardiol] / [Tier 2: e.g., JACC Cardiovasc Imaging] / [Tier 3: e.g., Eur Radiol]
- **Paradigm spec**: see PARADIGM.md (Phase 0 output)
- **Citation integrity**: see references/CITATION_INTEGRITY.md
- **Hallucination self-check**: see references/HALLUCINATION_PATTERNS.md
## Terminology Standardization
| Unified Term | Avoid Using |
|---|---|
| coronary CT angiography (CCTA) | cardiac CT, CT angio, CTA (ambiguous) |
| centerline (anatomical structure) | midline, vessel line |
| [add domain-specific terms] | [variants to avoid] |
## Canonical Paper / Dataset Descriptions
To prevent internal inconsistency (HALLUCINATION_PATTERNS.md ▸ Pattern 9), lock in canonical descriptions:
| Paper / Dataset | Canonical description |
|---|---|
| ImageCAS | 1000 cases, single-center, CCTA artery segmentation |
| TransCC (Xu et al.) | 3 authors: Chenchu Xu, Meng Li, Xue Wu; Dice 0.730 / IoU 0.582 on ImageCAS |
| FISH&CHIPS | NHS national CT-FFR implementation program; publication: Fairbairn TA et al. Nat Med 2025;31(6):1903-1910 |
| [add high-value papers] | [verified description] |
## Reference Sources
### ArXiv MCP
Search queries used:
- "[topic] segmentation transformer" (cs.CV, eess.IV)
- "[topic] deep learning" (cs.LG)
Date range: last 3 years
Downloaded papers: [list arXiv IDs]
### PubMed MCP
MeSH queries used:
- "Deep Learning"[MeSH] AND "[domain]"[MeSH]
- "[method]"[MeSH] AND "diagnosis"[MeSH]
Filters: Review, Clinical Trial
### Zotero
API: localhost:23119
Relevant collections:
- [Collection name 1]: [N papers]
- [Collection name 2]: [N papers]
## Literature inventory (3-axis grouping)
### Axis 1: Architectural priors
- **CNN-based**: [list verified refs]
- **Transformer-based**: [list]
- **Mamba / state-space**: [list]
### Axis 2: Inductive priors
- **Topology-aware**: [list]
- **Multi-task**: [list]
- **Graph-based**: [list]
### Axis 3: Data regime
- **Self-supervised**: [list]
- **Foundation models**: [list]
- **Federated**: [list]
- **Physics-informed**: [list]
### Clinical / downstream
- [list]
## Verdict positions to take (3-5)
Plan the 3-5 verdict sentences in advance:
- §Methods axis 1 closer: ...
- §Methods axis 2 closer: ...
- §Methods axis 3 closer: ...
- §Clinical translation closer: ...
- (optional) §Discussion closer: ...
## Writing-time guardrails (from references/)
- Per-claim verification on every `[N]` placed (CITATION_INTEGRITY ▸ Rule 1-5)
- Self-scan against 9 hallucination patterns every 5-6 paragraphs (HALLUCINATION_PATTERNS.md)
- Vendor names only in Table 3
- Equations only in Box 1
- Max 2 heading levels, no numbered headings
# Implementation Plan: [Review Title]
## Phase 0: Paradigm Capture
**Status**: [Not Started / In Progress / Complete]
- [ ] Identify 2-3 exemplar reviews from target tier
- [ ] Read carefully (60-90 min each)
- [ ] Extract style spec to PARADIGM.md
- [ ] Re-anchor at start of each Phase 4 writing session
## Phase 1: Project Initialization
**Status**: [Not Started / In Progress / Complete]
- [ ] CLAUDE.md created with terminology + sources + literature inventory skeleton
- [ ] IMPLEMENTATION_PLAN.md (this file) created
- [ ] manuscript_draft.md created (empty)
- [ ] PARADIGM.md linked
## Phase 2: Literature Collection + Verification
**Status**: [Not Started / In Progress / Complete]
### ArXiv (Deep Learning Methods)
- [ ] Search "[topic] segmentation" in cs.CV, eess.IV
- [ ] Search "[topic] transformer/attention" in cs.CV
- [ ] Download key papers (target: 50-80 — discriminate aggressively)
- [ ] Read each (abstract + methods + results)
- [ ] Note actual module names + actual numbers in working notes
### PubMed (Clinical Literature)
- [ ] Search MeSH: "Deep Learning" AND "[domain]"
- [ ] Filter by publication type (Review, Clinical Trial)
- [ ] Collect clinical validation studies (target: 20-40)
- [ ] PubMed WebFetch on each for metadata
### Zotero (User's local library)
- [ ] Check existing collections for closed-access papers
- [ ] Note Zotero item keys for later fulltext retrieval
### Additional sources
- [ ] WebSearch supplementary (IEEE Xplore, Springer)
### Verification (every entry, before adding)
- [ ] DOI resolves on Crossref
- [ ] First + last author verbatim from first-source
- [ ] Journal, year, vol, issue, pages match
- [ ] No `xxx` / `[TBD]` placeholders
### Gap analysis
- [ ] Negative trials covered? (LLM bias: only positive)
- [ ] Inter-vendor reproducibility covered?
- [ ] Demographic-bias studies covered?
- [ ] Recent 6-month preprints covered?
## Phase 3: Outline + 3-Axis Taxonomy
**Status**: [Not Started / In Progress / Complete]
- [ ] Top-level sections defined (Introduction / Methods / Applications / Translation / Discussion)
- [ ] §Methods structured as **3 H3 subsections** (3 axes):
- [ ] §Methods.Architectural priors
- [ ] §Methods.Inductive priors
- [ ] §Methods.Data regime
- [ ] Each paper in literature inventory mapped to an axis
- [ ] Table 1 / 2 / 3 designs planned
- [ ] Box 1 (metrics) content planned
- [ ] Figures (3-5) planned
- [ ] Verdict positions for 3 axes selected
## Phase 4: Write with Per-Claim Verification
**Status**: [Not Started / In Progress / Complete]
For each section, micro-workflow (per CITATION_INTEGRITY.md):
- [ ] Re-read the actual cited papers
- [ ] Write 2-4 sentences with actual module names + actual numbers
- [ ] Verify each `[N]` placed (body↔bib, number, direction)
- [ ] Close axis with verdict sentence
- [ ] Equations to Box 1, not body
- [ ] Vendor names to Table 3, not body
- [ ] Self-scan against 9 hallucination patterns every 5-6 paragraphs
### Section progress
- [ ] Introduction (Clinical background / Technical challenge / Scope)
- [ ] Datasets and evaluation metrics (Box 1, Table 1)
- [ ] Methods §Axis 1: Architectural priors
- [ ] Methods §Axis 2: Inductive priors
- [ ] Methods §Axis 3: Data regime
- [ ] Methods Table 2 finalized
- [ ] Downstream applications
- [ ] Translation to clinical practice (Table 3)
- [ ] Outstanding challenges
- [ ] Future directions
- [ ] Conclusion
- [ ] References (cross-checked with body)
## Phase 5: Multi-Agent Peer Review
**Status**: [Not Started / In Progress / Complete]
- [ ] TeamCreate `manuscript-review`
- [ ] Spawn `style-reviewer` teammate (Task 1)
- [ ] Spawn `ref-checker` teammate (Task 2)
- [ ] Spawn `peer-reviewer` teammate (Task 3)
- [ ] Spawn `fact-checker` teammate (Task 4)
- [ ] Synthesize 4 reports into 00_team_synthesis.md
- [ ] Address all CRITICAL findings
- [ ] Address HIGH findings agreed by ≥ 2 reviewers
## Phase 6: Submission Prep
**Status**: [Not Started / In Progress / Complete]
- [ ] Journal selected (reach + match + safety tiers)
- [ ] Presubmission inquiries sent (reach tier, optional)
- [ ] Cover letter drafted
- [ ] Box vs body duplication scan run
- [ ] Section cross-reference scan run
- [ ] Figures produced (not placeholders)
- [ ] Citation format converted for target journal
- [ ] Final self-check checklist passed
## Literature Sources Summary
| Source | Query / Collection | Papers | Verified | Status |
|---|---|---|---|---|
| ArXiv | [query 1] | N | N | [ ] |
| ArXiv | [query 2] | N | N | [ ] |
| PubMed | [MeSH query] | N | N | [ ] |
| Zotero | [collection] | N | N | [ ] |
## Change log
### [Date] - v1.0
- Initial draft completed through Phase 4
### [Date] - v1.1
- Phase 5 peer review found 8 issues; addressed
### [Date] - v1.2
- Phase 6 submission prep done; sent to [Journal]
**Table 1 | Public datasets for [task].**
| Dataset | Year | Cases | Annotation type | Access |
|---|---|---|---|---|
| [Name] | 20XX | N | [type] | [link] |
*Cases = unique patients. Where multi-center, the number of sites is given in parentheses. Access notations: O = open, R = restricted (registration), C = commercial.*
**Table 2 | Representative deep learning methods for [task].**
| Reference | Modality | Family | Architecture | Dataset | Headline metric | Innovation |
|---|---|---|---|---|---|---|
| [First-author Year] [N] | CCTA | Topology-aware | nnU-Net + clDice | ImageCAS | Dice 0.812 | First clDice on CCTA |
*Performance numbers are taken from the original publications and should not be compared directly across rows; datasets, splits and metric definitions differ. Family is the most-engaged inductive prior or training regime, not the only one. CCTA = coronary CT angiography; HD95 = 95th-percentile Hausdorff distance; — = not reported.*
**Table 3 | Commercial AI tools for [domain] with regulatory clearance.**
| Manufacturer | Product | Indication | Underlying technology | Regulatory | Deployment | Key validation evidence |
|---|---|---|---|---|---|---|
| HeartFlow | CT-FFR Analysis | CT-FFR | CFD + DL hybrid | FDA cleared (CPT 75580); CE | Cloud-based SaaS | NXT, ADVANCE, PLATFORM, PACIFIC; FISH&CHIPS [N] |
| Keya Medical | DeepVessel FFR | CT-FFR | DL surrogate of CFD | FDA, NMPA Class III, CE | Cloud + on-site | Multi-centre Chinese validation [N] |
*Listed validation evidence refers to peer-reviewed primary publications, not vendor white papers. NMPA = National Medical Products Administration (China); CFD = computational fluid dynamics.*
v2 set "80-120 references" as a quality criterion. This drove Claude to pad the bibliography, and padding encouraged fabrication. v3 has no count target. Cite what supports the argument. If your review naturally has 60 high-quality refs, that's enough. If it needs 150, also fine. Quantity is downstream of substance.
v2's coronary review produced 10 flat method H3 subsections. The resulting §Methods read as a textbook chapter, not a flagship review. Flagship reviews compress 10+ method variants into ~3 thematic axes that force comparative synthesis. The 3-axis structure is the most important structural fix in v3.