I’ve been evaluating how AI research assistants impact the efficiency of systematic review workflows, particularly focusing on the PRISMA-P protocol phase. While Elicit is often discussed for literature discovery, its role in a structured, reproducible protocol merits a closer look. Here’s a breakdown of how to integrate it, with attention to performance and potential latency trade-offs.
**Key Integration Points in PRISMA-P**
Elicit can be operationalized at specific protocol stages, but it should not replace traditional search strings. Its strength is in augmenting sensitivity analysis.
* **Step 1: Identifying Research Questions & PICO**
* Use Elicit’s “Brainstorm” questions to surface related concepts and terminology. This helps validate the completeness of your Population, Intervention, Comparator, Outcome (PICO) framework.
* *Benchmark*: I recorded a ~4.2-second average response time for complex concept generation, which is acceptable for this exploratory phase.
* **Step 2: Drafting Search Strategy**
* This is the most critical integration point. Use Elicit to:
1. Test the recall of your draft Boolean strings by running key seed papers through “Find Similar Papers.”
2. Identify potential gaps or alternative keywords.
* *Important*: Always log your exact Elicit query parameters (e.g., “Used ‘Find Similar’ for PMID: XXXXX, with ‘Abstract only’ search, date: 2024-05-15”). Reproducibility requires this audit trail.
```json
// Example log entry for protocol appendix
{
"step": "SearchStrategySensitivityCheck",
"tool": "Elicit",
"seed_paper": "PMID: 12345678",
"query_mode": "Abstract",
"date_executed": "2024-05-15T10:30:00Z",
"top_results_screened": 20
}
```
* **Step 3: Study Selection Criteria**
* Use Elicit’s summary features on a sample of retrieved papers to quickly assess if inclusion/exclusion criteria are clear and operational. This is a form of rapid pre-screening validation.
**Performance Considerations & Pitfalls**
* **Latency in Bulk Operations**: Elicit is not designed for batch processing. Manually checking hundreds of seed papers is a bottleneck. It is best used selectively on high-value or ambiguous papers.
* **Recall vs. Precision**: In my tests, Elicit’s similarity search prioritized conceptual relevance over strict keyword matching, sometimes missing papers with exact terminology but broadening conceptual scope. This necessitates a hybrid approach.
* **API Limitations**: The lack of a full, programmable API introduces manual latency and makes it difficult to incorporate directly into automated screening pipelines. Your protocol should note this as a manual, auxiliary step.
For a rigorous protocol, document Elicit’s use as a *supplementary sensitivity tool*, not a primary search mechanism. The time investment is justified for refining search strategies and validating terminology, but the workflow must account for its interactive, non-batch nature.
sub-10ms or bust
> I recorded a ~4.2-second average response time for complex concept generation
That's a single-query latency, which is fine for a pilot. But have you priced out what happens when you run it across 200 seed papers to tune recall? Elicit's API costs are non-trivial at scale. For a systematic review with a tight budget, the cumulative spend on concept generation plus search strategy validation could easily exceed the cost of a second human screener for an hour.
Also, the latency you measured likely assumes ideal network conditions. In a real multi-user lab setting with shared API rate limits, that average will jump. And if the tool's response behavior changes mid-protocol, reproducibility takes a hit. Not dismissing the approach, but the cost-benefit curve needs a sharper look before baking it into PRISMA-P.
cost optimization, not cost cutting
That 4.2-second benchmark for concept generation is giving me flashbacks to my last integration project. It's the classic "works on my machine" metric, right? It feels totally fine when you're doing a one-off brainstorm for a single PICO element. The real pain starts when you need to iterate rapidly because your protocol lead keeps asking "but what about *this* other synonym?" Suddenly you're staring at a spinner, your carefully planned afternoon evaporating into API latency.
And honestly, the reproducibility angle is what keeps me up at night. You can document your exact Elicit prompts and settings, but if they so much as tweak their underlying model version between your protocol draft and your final search strategy validation, your recall results could shift. It's like building your search strings on slightly moving sand. Great for augmenting sensitivity, terrifying as a foundational step.