> ## Documentation Index
> Fetch the complete documentation index at: https://docs.consensus.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Find the leading investigators in an indication

> Rank the authors publishing most in a therapeutic area, split by region and by recent versus lifetime output.

## What you get

A ranked investigator list with paper counts, representative citations, recency, and region — built from the literature rather than from whoever your team already knows.

## Who it's for

Medical affairs selecting advisory boards and speakers, clinical operations shortlisting trial sites, and business development mapping who owns a space.

<Note>
  Consensus returns **author names**, not affiliations, institutions, or contact details. This produces a ranked name list you then join to a CRM, an institutional directory, or a trial registry. Treat it as the discovery step, not the whole workflow.
</Note>

## How it works

<Steps>
  <Step title="Sweep the indication widely">
    Fan out across the indication, its mechanisms, its endpoints, and its main comparators. Paginate — emerging investigators sit in the long tail, and the top 20 results only ever show you the established names.
  </Step>

  <Step title="Count authors across the deduplicated set">
    Tally every name, weighting by `citation_count`, recency, and `sjr_best_quartile`. Track first and last author positions separately where you can infer them: last author usually signals the lab, first author the person doing the work.
  </Step>

  <Step title="Rank twice">
    Once by lifetime output, once by output in the last 24 months. The two lists differ substantially, and the second is usually the more actionable — it tells you who is active now rather than who was.
  </Step>

  <Step title="Add geography">
    Use `country` to build per-region shortlists. A single global list is rarely what a regional medical team can act on.
  </Step>

  <Step title="Read one paper per name">
    Output volume is not fit. Check that their actual work matches the specific question before anyone gets contacted.
  </Step>
</Steps>

## The API call

```bash theme={null}
curl -G "https://api.consensus.app/v1/search" \
  -H "x-api-key: $CONSENSUS_API_KEY" \
  --data-urlencode "query=CAR-T therapy relapsed refractory multiple myeloma outcomes" \
  --data-urlencode "human=true" \
  --data-urlencode "year_min=2023" \
  --data-urlencode "country=us,gb,de" \
  --data-urlencode "page_size=200"
```

```python theme={null}
from collections import Counter
authors = Counter()
for paper in results:
    weight = 1 + (paper.get("citation_count", 0) ** 0.5) / 10
    for name in paper.get("authors", []):
        authors[name] += weight
```

## What to check before you trust it

* **Name ambiguity is the main failure.** Common surnames merge distinct researchers, and the same person appears under several spellings. Verify anyone you plan to act on.
* **Volume rewards large consortia.** Authors on big multi-site trials accumulate counts without being the intellectual lead. Weight last-author position where you can.
* **Check the recency split before using the lifetime list.** Highly-cited historical names may have left the field entirely.
* **This is a discovery list, not a vetted one.** Conflicts of interest, availability, and current affiliation all need checking elsewhere.

## Related

<CardGroup cols={2}>
  <Card title="Monitor new research about a drug, device, or brand" icon="satellite-dish" href="/use-cases/brand-and-device-monitoring">
    Keep watching the space and the names in it.
  </Card>

  <Card title="Build a drug target validation dossier" icon="dna" href="/use-cases/target-evidence-tiers">
    Go deep on the science these investigators are publishing.
  </Card>

  <Card title="Best practices" icon="shapes" href="/use-cases/best-practices">
    Fan-out and merge, and the rest of the primitives.
  </Card>

  <Card title="All use cases" icon="grid-2" href="/use-cases">
    Browse the gallery by persona.
  </Card>
</CardGroup>