Meta Feasibility Analyzer

Source: https://github.com/aipoch/medical-research-skills

Meta Feasibility Analyzer

This skill evaluates the feasibility of conducting a new Meta-analysis on a given topic (title). It checks for existing Meta-analyses and available Clinical Trials to determine if there is a gap or sufficient new evidence.

When to Use

• Use this skill when you need analyzes the feasibility of a proposed meta-analysis topic by searching for existing meta-analyses and clinical trials on pubmed/clinicaltrials.gov. use when you need to evaluate if a topic is viable for a new meta-analysis in a reproducible workflow.
• Use this skill when a data analytics task needs a packaged method instead of ad-hoc freeform output.
• Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
• Use this skill when scripts/feasibility_ops.py is the most direct path to complete the request.
• Use this skill when you need the meta-feasibility-analyzer package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Use when you need to evaluate if a topic is viable for a new Meta-analysis.
• Packaged executable path(s): scripts/feasibility_ops.py.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Data Analytics/meta-feasibility-analyzer"
python -m py_compile scripts/feasibility_ops.py
python scripts/feasibility_ops.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/feasibility_ops.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/feasibility_ops.py.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Workflow

Follow these steps to perform the analysis.

1. Generate Search Query

First, analyze the user's proposed title to generate a valid PubMed search query.

Prompt for LLM:

Role: Medical Search Expert
Task: Extract keywords from the following title and create a PubMed search query.
Title: "{{input_the_title}}"

Rules:
1. Extract keywords (Disease, Intervention, Outcome).
2. Convert to standard MeSH terms if possible.
3. Combine with AND/OR.
4. Enclose the final query in braces {}.
5. Do NOT include "meta analysis" in the query.

Example Output:
{(ovarian cancer) AND (chemotherapy) AND (bevacizumab)}

2. Extract Query String

Run the extraction script to get the clean query string.

python scripts/feasibility_ops.py extract --text "{{llm_output}}"

Store the output as {{search_query}}.

3. Search Clinical Trials

Search for Clinical Trials via the PubMed API.

python scripts/feasibility_ops.py search --query "{{search_query}}" --type clinical

Store the result JSON as {{clinical_json}}.

4. Process Clinical Results

Format the clinical trial results and check the count.

python scripts/feasibility_ops.py clinical --json '{{clinical_json}}' --query "{{search_query}}"

Parse the output JSON to get:

• clinical_count: Number of trials found.
• clinical_summary: Formatted summary string.

5. Feasibility Check (Stage 1)

If clinical_count == 0:

• The topic is NOT FEASIBLE due to lack of primary studies.
• Output: "⚠️ Sorry, no relevant clinical studies found for this title. This topic is likely not feasible."
• STOP.

If clinical_count > 0:

• Proceed to Step 6.

6. Search Meta-Analyses

Search for existing Meta-analyses via the PubMed API using the same query.

python scripts/feasibility_ops.py search --query "{{search_query}}" --type meta

Store the result JSON as {{meta_json}}.

7. Process Meta Results

Format the meta-analysis results.

python scripts/feasibility_ops.py meta --json '{{meta_json}}'

Parse the output JSON to get:

• meta_summary: Formatted summary string.

8. Final Feasibility Analysis

Analyze the results to determine final feasibility.

Prompt for LLM:

Role: Clinical Research Expert
Task: Assess Meta-analysis feasibility.

Input:
Title: "{{input_the_title}}"
Existing Meta-Analyses:
{{meta_summary}}

Existing Clinical Trials:
{{clinical_summary}}

Logic:
1. If NO existing Meta-analyses + YES Clinical Trials -> ✅ FEASIBLE.
2. If YES existing Meta-analyses:
   - Check the dates. Are there new Clinical Trials published AFTER the latest Meta-analysis?
   - If YES new trials -> ✅ FEASIBLE (Update is possible).
   - If NO new trials -> ⚠️ NOT FEASIBLE (Already covered).

Output Format:
"{{input_the_title}}"
[Conclusion: ✅ Feasible / ⚠️ Not Feasible]
Reason: [Explain based on the logic above]

9. Output

Present the final analysis to the user.