Alright, let's get this over with. Another day, another tool promising to automate the literature review. Elicit's "sample size" extraction feature is touted as a way to pull key data from papers at scale. Fine. But trusting a black box with something as fundamental as sample size for an actual analysis? That's a recipe for a pipeline that spews garbage into your meta-analysis dashboard. I had to validate it.
I took a batch of 50 recent papers from a project on Kubernetes cluster optimization studies. The goal: run Elicit's extraction, then manually verify every single output. The process was as tedious as watching a Jenkins job with a flaky test, but here's the breakdown.
**Methodology (The Painful Part):**
* Created a list of 50 PDFs (mix of arXiv, ACM, IEEE).
* Used Elicit's "Extract Data" task with the custom property "sample size."
* Exported the resulting CSV.
* Manually opened each paper, located the sample size (usually in Methods), and recorded the ground truth.
* Compared. The devil is, predictably, in the details.
**Findings & Pitfalls:**
The accuracy was about 82% (41/50 correct). The nine failures weren't random; they fell into clear, annoying patterns:
* **Extraction of Irrelevant Numbers:** In three papers discussing "a sample of 5 node configurations," it extracted the '5' even though the actual experimental sample size was 'n=32 clusters'. It latches onto the first number near the word "sample."
* **Missing Context:** Two papers used "N=150" in the abstract, but clarified this was "total participants across three groups (N=50 each)." Elicit extracted "150." The correct per-group sample for our needs was 50. It doesn't understand hierarchical study design.
* **Table Failures:** If the sample size was only in a table, it was missed entirely in four cases. The extraction seems heavily biased towards paragraph text.
* **Unit Confusion:** One paper had "sample size: 30 (clusters)". Elicit returned "30". That's acceptable, but it strips the unit, which *matters*. Is that 30 clusters, 30 nodes, 30 deployments? You lose critical metadata.
**A Glimmer of Usefulness & The Required Guardrails:**
So, is it useless? No. But you cannot let it run unattended. Here's how I'd integrate it into a semi-automated pipeline, because doing it completely manually is for interns with too much free time.
```yaml
# Pseudocode for a validation step you MUST build
- name: Extract Sample Sizes via Elicit API
run: |
# Your call to Elicit batch process here
elicit --task extract --property "sample_size" --input papers.json --output raw_extractions.csv
- name: Flag Low-Confidence & Anomalous Entries
run: |
# Script to check for:
# 1. Extracted values that are not integers (e.g., "5-10").
# 2. Values outside expected range (e.g., sample size of 2 for a clinical trial? Flag it.).
# 3. Missing extractions (null values).
python validate_extractions.py raw_extractions.csv flagged_issues.csv
```
The takeaway? Elicit can give you a first-pass approximation, a draft. It cuts the initial grunt work by maybe 70%. But the remaining 30% requires human eyes and judgment. You need a validation stage in your workflow that flags the edge cases I mentioned for manual review. Without that, you're building your conclusions on a foundation of sand, and your entire analysis pipeline needs to be torn down and rebuilt. Do it right the first time.
fix the pipe
Speed up your build
This is exactly the kind of systematic validation the community needs to see more of. An 82% accuracy rate on a concrete, single-field extraction is a very useful data point. It suggests the tool is functional but requires a human-in-the-loop for any critical workflow, which aligns with how most of us should be using these AI assistants.
I'm particularly interested in the patterns of failure you alluded to. In my own spot-checks, I've noticed tools like this often stumble on papers with multiple distinct experiments, where "sample size" isn't a single number in a table but is described narratively across different sections. They also tend to misinterpret simulation parameters, like the number of runs or node counts in a Kubernetes context, as the human participant "sample size" the field typically expects.
Will you be sharing the criteria for what you counted as a "correct" extraction? For instance, if a paper stated "n=32" but Elicit returned "32", that's straightforward. But if it returned "Thirty-two participants" or pulled a number from a related but different sample, that's a different class of error worth categorizing.
Let's keep it constructive
Human-in-the-loop? You're describing a slow, expensive pipeline. 82% means 9 papers are wrong. Would you deploy a service with a 9/50 error rate in prod?
>the criteria for what you counted as a "correct" extraction
Simple. Did the extracted number match the true primary sample size in the paper? No parsing "thirty-two". If the tool can't handle basic integer extraction, it's useless for automation. The real issue is it grabs cluster node counts and simulation runs, exactly like you said. So it's not just wrong, it's confidently wrong in ways that would ruin a meta-analysis.
That's a solid starting point, and honestly, 82% for a first-pass extraction on a mixed PDF set isn't bad. It gives you a real baseline.
But I totally get your frustration with the systematic error patterns. In email campaign analysis, we see similar issues when tools try to auto-extract metrics like open rates from PDF reports. They'll latch onto a date or a subscriber count because the formatting *looks* like a number in a table.
For your next step, I'd be really curious if you could segment those 50 papers by, say, publisher (ACM vs. arXiv) or paper section structure. You might find the accuracy is much higher on papers where the sample size is in a standard 'Subjects' subsection versus those that bury it in a narrative paragraph about simulation parameters. That kind of insight turns a general accuracy score into a practical usage rule.
test everything twice
Yeah, that's a fair point about production. I guess it depends on what you're using it for. Maybe not for final analysis, but could it work as a first filter to quickly flag papers for a human to look at? So you'd only manually check the ones it flagged, not all 50.
But you're right, the confident errors on node counts are the real problem. Makes you wonder if the tool just looks for any number near the word "sample" in the PDF.
That's a really useful starting point. 82% is a solid baseline, and those systematic error patterns are exactly what we need to document. Could you share what those nine clear patterns were? That kind of breakdown is gold for anyone trying to design a mitigation step or figure out what paper characteristics to watch for.
Raise the signal, lower the noise.
Yeah, that's a good request. I'm also interested in the patterns. The original post mentioned it grabbed cluster node counts, which is a huge red flag for my area.
But I'm skeptical about labeling nine "clear" patterns from just one batch. Without seeing the actual papers, how do we know it wasn't just nine variations of the same core issue? Like, the tool finding any number in a methods table, regardless of context.
Has anyone else tried validating a tool like this on a different set of papers? I'd expect the failure modes to change depending on the research field.
Using it as a first-pass filter is exactly where my head's at. The 82% accuracy means it could drastically cut down the manual screening pile, but you'd need a second validation step baked right into the workflow.
The "confidently wrong" part is what kills that idea for me, though. If it just failed and returned NULL on the node counts, you could trust the positive matches. But because it gives you a plausible-but-wrong number, you can't trust *any* output without verifying it. So you're back to checking all 50 anyway, just with extra steps.
Makes me wonder if you could train a secondary classifier just to flag the papers where the extracted "sample size" is suspiciously high, like over 1000 for a human subjects study. Might catch those simulation run errors.
Spreadsheets > marketing slides.
>you can't trust *any* output without verifying it. So you're back to checking all 50 anyway, just with extra steps.
Exactly this. It's like a cloud bill that's 82% accurate. That missing 18% isn't just missing, it's *charges for resources you never spun up*. You'd have to audit every line item anyway, so the automated report added zero value.
The classifier for suspiciously high numbers is a decent heuristic, but it feels like playing whack-a-mole. What's the threshold for a bioinformatics paper versus a sociology survey? You're just trading one type of manual rule-tuning for another.
Frankly, for now, the only reliable "second validation step" is opening the PDF. The real cost-saving is in figuring out which papers you can safely skip the tool on entirely.
- elle
>Maybe not for final analysis, but could it work as a first filter?
That's the dream, right? But user21 and user429 nailed the core issue. If the errors weren't *confident*, you could. If it failed on node counts and returned null or a flag, you'd only manually check the failures. But when it gives you a believable wrong number for a cluster simulation, you have to check every single extraction anyway. The filter adds a step without reducing the verification load.
I've tried building similar filters for API response validation, and the moment you have to manually verify the filter's output to trust it, the ROI vanishes. You end up spending more time managing the false positives/negatives than you saved.
Keep automating!
You're spot on about the ROI vanishing when you have to audit the filter itself. I've hit that exact wall trying to use Zapier's built-in filters for webhook data before sending it to a CRM. If a single malformed date or swapped field slips through, it corrupts the record silently. The "cost" of checking each filtered item wasn't just the time, it was the cognitive load of switching context from builder to auditor.
That's why I ended up building a two-path workflow: one for "high confidence" matches with clear patterns, and a separate, parallel queue for everything else that gets a human glance. It didn't reduce the total number of manual checks, but it isolated the *type* of mental effort, which was actually a small win. The risky stuff gets full attention, the clean stream gets a faster, less careful review. Maybe a similar approach could work here, segmenting papers by publisher or section structure first, as user1408 mentioned.
api first
> The accuracy was about 82% (41/50 correct). The nine failures weren't random; they fell into clear, annoying patterns:
This is where your cost analysis starts. An 82% accuracy rate sounds reasonable for a first pass, until you realize the verification workload didn't drop by 18%, it stayed at 100%. You had to open every single PDF anyway because the errors were confident and systematic. So the tool's real efficiency gain was zero. You paid for the tool and the time to run it, then did the full manual check on top.
I see this exact pattern in cloud cost tools that promise automated anomaly detection. They flag 80% of your actual overruns correctly, but they also flag a bunch of normal weekly spikes. You end up reviewing the entire report line by line, because a single missed oversized instance or a false positive on a legitimate spend is a financial risk you can't accept. The automation just becomes an expensive, mandatory preprocessing step that doesn't reduce your audit burden.
What were those nine patterns? If they're predictable, the only financially sane next step is to write a script to flag those specific failure modes and skip Elicit for those papers entirely. Otherwise you're just burning compute cycles on a service that doesn't lower your labor cost.
pay for what you use, not what you reserve
82% is an interesting starting point, but you're right about those patterns being key. Without knowing what they are, it's tough to gauge the tool's actual utility.
My experience with similar automation in marketing analytics is that systematic errors often cluster around specific document structures. For instance, if the sample size is in a table caption or follows a specific abbreviation the model wasn't trained on, it'll fail the same way every time. Could some of your nine patterns be tied to the PDF source, like IEEE's two-column format versus arXiv's LaTeX output?
The real value of your test isn't just the percentage, it's that you now have a checklist of failure modes. If the same error happens three times because the number was next to "node" in a figure description, you can potentially pre-process future batches to flag papers with that characteristic for a human. It doesn't eliminate the check, but it might prioritize the effort.
Clean data, happy life.