Our research lab is currently evaluating AI-powered research assistants to systematize the initial screening and data extraction phases for systematic literature reviews. The primary contenders are Consensus and Elicit. Our team consists of five postdoctoral researchers, each managing 2-3 concurrent reviews, typically involving screening 5,000-15,000 paper abstracts per review. We require precise metadata extraction and consistent quality across users.
We conducted a controlled benchmark over a four-week period, using a shared corpus of 1,200 pre-labeled abstracts from prior reviews in biomedical engineering. Both tools were tasked with answering specific, structured queries and extracting defined data points. Our analysis focused on the following dimensions:
* **Query Precision & Recall:** For a query like "effect of graphene oxide on osteoblast proliferation in vivo," we measured the percentage of relevant papers correctly identified (recall) and the proportion of suggested papers that were actually relevant (precision).
* **Data Extraction Accuracy:** We defined a schema for extraction (e.g., sample size, organism model, outcome metric, p-value) and compared tool output against our manual gold standard.
* **Workflow Integration & API Viability:** Assessed the ability to automate bulk processing, the quality of exported data structures (CSV, JSON), and API reliability for programmatic access.
* **Cost-Performance at Scale:** Analyzed the effective cost per screened/processed paper under our typical usage patterns, including subscription tiers and credit-based systems.
**Preliminary Data Summary (Averaged across 5 test queries):**
| Metric | Consensus | Elicit | Notes |
| :--- | :--- | :--- | :--- |
| **Precision (Top 20)** | 78% (±6%) | 85% (±4%) | Elicit's stricter semantic matching yielded fewer false positives. |
| **Recall (Estimated)** | ~92% | ~88% | Consensus consistently surfaced a broader, more inclusive set. |
| **Data Extraction F1 Score** | 0.71 | 0.82 | Elicit was superior at parsing complex sentences into structured fields. |
| **Bulk Processing Speed** | 100 abstracts/min | 40 abstracts/min | Consensus's dedicated bulk search is notably faster. |
| **API Stability (Uptime)** | 99.2% | 99.8% | Both were reliable; Elicit had marginally fewer timeouts. |
| **Cost per 1000 abstracts** | ~$12 (Team plan) | ~$28 (Pay-as-you-go) | Consensus's flat-rate pricing is more predictable for high volume. |
**Key Technical Observations:**
* **Consensus** leverages a proprietary search stack atop Semantic Scholar. Its strength is comprehensive retrieval speed and cost-effective bulk operations. However, its extraction capabilities are less nuanced. The output often requires additional cleaning, as it tends to pull verbatim text snippets rather than normalizing data into consistent units. Its Boolean query support is more flexible for expert searchers.
* **Elicit** is built on finer-tuned language models specifically for reasoning about research methodology. Its extraction is its standout feature; it reliably identifies and normalizes concepts like study design, outcome measures, and quantitative results into a usable table. However, its search is more conservative and slower, which can impact initial screening throughput.
**Open Questions for the Community:**
1. For labs with similar profiles, has anyone implemented a hybrid workflow? For instance, using Consensus for the initial high-recall screening phase, then passing a subset to Elicit for detailed data extraction?
2. We encountered challenges with both tools regarding non-English study filtering and handling of pre-print archives (e.g., arXiv, bioRxiv). How have others configured queries or post-processing pipelines to manage this?
3. The API response schemas differ significantly. Elicit's is more structured, while Consensus's is more focused on citation metadata. Are there any open-source parsers or middleware to normalize output from these services into a common schema (e.g., PRISMA-compliant CSV)?
I'm a computational biology lab manager at a mid-sized university, running a similar five-person team focused on clinical meta-analyses. We use Elicit in production for the initial abstract screening phase across about ten concurrent systematic reviews in oncology.
* **Query Precision for Complex Biomedical Terms:** Elicit demonstrated a 12-18% higher precision rate in our benchmark on queries with composite materials (like your graphene oxide example). Consensus often misclassified papers discussing general "carbon nanomaterials" as relevant, requiring significant post-filtering. This directly impacts your team's screening burden.
* **Structured Data Extraction Accuracy:** For defined schemas with 5-10 fields, Elicit's accuracy on our labeled set was 91-94% on the first pass. Consensus averaged 82-87%. The gap was largest for numerical data (p-values, sample sizes), where Elicit's parsing was more consistent. However, both require a human verification pass for publication-grade extractions.
* **Cost Structure and Scale:** Consensus uses a simpler per-user subscription (around $8-12/user/month). Elicit's primary cost is based on a monthly credit system for "tasks" (like a screening query or extraction). For your volume (5 users, 2-3 reviews each, 5k-15k abstracts), you'd likely need their Team plan, which we pay $300/month for, allowing ~10,000 screening tasks. If your abstracts/review are on the higher end, budget for an additional $100-150/month in overage credits.
* **Workflow Integration and Consistency:** Elicit provides a shareable "workspace" where your team's query history and extracted data are synchronized, which is critical for audit trails and preventing duplicate work. Consensus sessions are more user-isolated. For a five-user lab ensuring consistent protocols, Elicit's shared environment reduced our coordination overhead by about a third.
I'd recommend Elicit for your specific use case of structured data extraction and multi-user review management, given its higher precision on technical queries and built-in team coordination. The choice would hinge entirely on your strict monthly budget; if $300/month is prohibitive, Consensus is a functional, more affordable starting point.
Show me the bill.
Interesting that you mention the cost model, because that's where the devil is truly hiding in these academic SaaS tools. You say Elicit's primary cost is based on monthly credits for "tasks," but that's a massive red flag for a lab handling 5,000-15,000 abstracts per review. A "task" is a wonderfully opaque unit of measure that can be quietly redefined at any time by the vendor, and your monthly bill becomes a direct function of your research volume, which can spike unpredictably.
With five postdocs each running multiple reviews, you're not just paying for seats, you're paying per unit of your own intellectual labor. That creates a perverse incentive to be frugal with queries or to batch work in ways that might compromise the review process. A simple per-user fee, like Consensus uses, at least gives you budget certainty, even if the tool itself is less precise. In a grant funded world, predictable overhead trumps a slight edge in precision if that edge comes with a variable, usage based invoice that could blow your quarterly allocation.
Your point about Elicit's higher accuracy for numerical data is well taken, but I'd be curious how that performance degrades when you scale to your full corpus, not just a benchmark set of 1,200. I've seen tools perform beautifully on a curated test batch only to introduce bizarre, intermittent parsing failures on larger, messier real world data, and that's where the real time sink is.
Trust but verify.
That's a solid benchmark approach, focusing on precision, recall, and extraction accuracy. A dimension I'd consider adding to your matrix is cost *variability*. With each postdoc handling thousands of abstracts, the volume is significant.
If your schema for "Data Extraction Accuracy" includes, say, five fields, you should calculate how many "tasks" or API calls Elicit requires to populate that schema for a single abstract. That volume multiplier can turn a per-user fee into a very different total cost picture, especially during peak periods. A spike in relevant papers shouldn't create budget anxiety.
It shifts the evaluation from just tool accuracy to total cost of the review process, which includes both labor for corrections and the actual SaaS spend.
Every dollar counts.
Great point about cost variability. It's not just about the per-abstract cost, it's how those tasks or calls actually stack up in a real workflow. One question though - how do you account for the labor cost of fixing lower accuracy? If Tool A costs less per abstract but needs more manual correction, isn't that just shifting the cost from the SaaS bill back to the postdoc's time?