How Academic Researchers Use Elicit's Data Extraction Tables to Compare Methodologies and Sample Sizes

For academic researchers conducting systematic literature reviews or meta-analyses, screening dozens or hundreds of PDF files to extract methodological details and sample sizes is one of the most grueling bottlenecks in evidence synthesis. Manually hunting through Methods and Results sections to verify whether a reported sample size reflects enrolled participants, randomized subjects, or final analyzed datasets—while cross-checking control types, blinding protocols, and intervention dosages—takes weeks of repetitive manual labor.

Elicit addresses this friction directly through its Data Extraction Tables. Integrated into both the dedicated Systematic Reviews workflow and the interactive Research Agent, this feature transforms unstructured academic PDFs into a structured comparison matrix. Instead of generating vague text summaries, Elicit extracts precise variables across dozens of papers simultaneously, provides sentence-level source citations for every extracted data point, and allows researchers to export clean datasets directly into statistical software or reference managers.

The Core Architecture: How Elicit Extracts Tabular Data

Understanding how Elicit extracts data helps researchers structure their queries and avoid common data retrieval pitfalls.

Full-Text Parsing vs. Abstract Limitations

Academic abstracts frequently omit granular methodological parameters, such as exact attrition rates, per-protocol participant counts, or specific laboratory assays. Elicit extracts data based on the text it has access to:

• Abstract-only extraction: If a paper lacks an attached PDF, Elicit extracts information solely from the abstract. If a specific sample subgroup or statistical test is not mentioned in the abstract, the column will return empty or incomplete information.
• Full-text PDF extraction: When Elicit has access to the full PDF, its models scan the entire document, including Methods, Results, footnotes, and embedded data tables.
• Acquiring full texts: Researchers can ensure full-text coverage by filtering search results with the "Has PDF" toggle, uploading their own PDF libraries, syncing with Zotero collections, or installing the Elicit Browser Extension to retrieve full texts through their university library proxy.

Table Reading and Premium PDF Parsing

Scientific papers often bury sample sizes and methodology controls in demographic tables (such as Table 1 baseline characteristics) rather than running narrative text. Elicit natively parses tables inside PDFs and displays the quoted table snippet directly in the verification panel.

For papers containing multi-column layouts, dense statistical summaries, or complex mathematical formulas, Elicit provides an account-level setting called Premium PDF parsing (available on Plus, Pro, Scale, and Enterprise plans). When enabled under Account Settings > Advanced, Elicit applies heavier vision and layout models that dramatically improve table transcription and formula reading. On Scale and Enterprise plans, turning on Premium PDF parsing also unlocks "Extract from figures," allowing the AI to extract data points plotted on graphs, flowcharts, and diagrams.

Step-by-Step Guide: Setting Up Comparison Tables for Methodologies and Sample Sizes

Systematic reviews require reproducible, auditable protocols. Here is the operational workflow researchers use to configure Elicit for cross-paper methodology and sample size comparison.

Step 1: Gathering and Screening Papers

Researchers can build their corpus within Elicit's Systematic Reviews workflow (recommended for PRISMA-compliant projects) or a Research Agent session:

1. Define the research question: Enter a focused question on the Setup page (for example, "Comparative efficacy of cognitive behavioral therapy versus mindfulness in adolescent anxiety").
2. Add eligibility context: Use the Additional Context field to define PICO criteria (Population, Intervention, Comparator, Outcomes) and explicit inclusion/exclusion rules.
3. Gather papers: Run semantic searches across Elicit's corpus of 138 million papers, PubMed, and ClinicalTrials.gov, or build reproducible Boolean search strings (AND, OR, NOT) using Keyword Search. Researchers can also upload existing RIS files or PDF batches.
4. Screen candidates: Elicit evaluates titles and abstracts against predefined criteria at Fast or Thorough reasoning depths. Enterprise teams can enable Dual Review to assign two independent human screeners and compute Cohen's kappa agreement statistics.

Step 2: Defining Custom Extraction Columns

Once the corpus is screened down to included studies, the researcher moves to the Data Extraction step. While Elicit automatically suggests columns based on the research question, comparative synthesis requires tailored custom columns.

To achieve consistent extraction, treat the AI like a human research assistant by configuring both a clean Column Name and detailed Instructions.

1. Custom Column: Analyzed Sample Size

• Column Name: Analyzed Sample Size (Total and By Arm)
• Instructions:

  Extract the final analyzed sample size at the end of the primary outcome evaluation.
  Distinguish clearly between:
  1. Total analyzed sample size (N_total)
  2. Intervention arm analyzed size (N_intervention)
  3. Control arm analyzed size (N_control)
  4. Number of dropouts or attrition count
  Do NOT extract the initially enrolled or randomized participant count unless the analyzed count is explicitly identical.
  Format your answer as:
  Total Analyzed: [N]; Intervention: [N]; Control: [N]; Dropouts/Loss to Follow-up: [N]
  

2. Custom Column: Study Design and Methodological Controls

• Column Name: Study Design and Blinding Protocol
• Instructions:

  Identify the specific study design and methodological rigor:
  1. Study Design: Classify as Double-blind RCT, Single-blind RCT, Open-label RCT, Prospective Cohort, Retrospective Cohort, or Quasi-experimental.
  2. Control Group Type: Placebo, active comparator, waitlist control, or treatment-as-usual (TAU).
  3. Blinding: State who was blinded (participants, intervention providers, outcome assessors).
  Format your answer as:
  Design: [Design Type]; Comparator: [Control Type]; Blinding: [Blinding Details]
  

3. Custom Column: Primary Outcome Measurement Protocol

• Column Name: Primary Outcome and Measurement Instrument
• Instructions:

  Extract the primary outcome measure, the clinical or psychometric scale used, and the primary follow-up assessment timeframe.
  Format as:
  Primary Outcome: [Name]; Instrument: [Scale Name]; Time Point: [e.g., 12 weeks post-baseline]
  

Step 3: Running a Pilot Extraction on 3 to 5 Papers

A standard best practice in evidence synthesis is running a pilot test before scaling to the entire dataset:

1. Select a representative subset of 3 to 5 diverse papers (e.g., one multi-arm RCT, one cluster RCT, and one observational cohort).
2. Run the extraction columns on this pilot subset.
3. Review the outputs to identify ambiguities. If Elicit confuses "enrolled participants" with "analyzed participants," refine the column instructions to add negative constraints (e.g., "Exclude ITT enrolled numbers if per-protocol analyzed counts are reported separately").
4. Once verified, click Save as Preset so the column definitions can be reused across future projects.

Step 4: Batch Extraction and Traceability Verification

After fine-tuning column prompts, execute the batch extraction across all screened-in papers.

The defining advantage of Elicit over generic language models is sentence-level verification:

• Clicking any cell in the extraction table immediately opens the source verification drawer.
• The drawer highlights the exact quote from the PDF text or reproduces the parsed table row where the data originated.
• If a study reports complex subgroup splits that require nuanced human interpretation, the researcher can edit the cell directly to record the adjudicated value.

Comparison Table: Recommended Column Blueprint for Systematic Reviews

The following template outlines high-utility extraction fields for comparative methodological reviews:

Exporting Data and Integrating into Meta-Analysis Software

Once all table columns are populated and verified, researchers must export the structured evidence into downstream statistical environments:

1. Export to CSV / Excel: Click the Export button at the top of the table. Elicit generates clean CSV and XLSX spreadsheets containing every custom column alongside bibliographic metadata (authors, publication year, DOI, journal).
2. Meta-Analysis Integration: The exported tabular data can be imported directly into R (packages such as meta or metafor), RevMan (Review Manager for Cochrane reviews), Stata, or SPSS for calculating pooled effect sizes, constructing forest plots, and running subgroup analyses based on sample size tiers.
3. Citation Management: Export references as .ris or .bib files to sync paper records directly into Zotero, EndNote, or Mendeley.

Subscription Plans and Cost-Benefit Considerations

Elicit structures its tiers around research volume and workflow depth:

• Basic (Free): Limited credits, basic abstract searches, and view-only access to legacy sessions. Not suitable for multi-paper systematic extraction.
• Plus ($10–$12/month): Provides access to the Research Agent, custom column creation, CSV/Excel table exports, and Premium PDF parsing. Suitable for independent scholars or graduate students conducting exploratory reviews on smaller paper sets (20–50 papers).
• Pro ($49/month): Unlocks the dedicated Systematic Reviews workflow, Boolean keyword search, up to 5,000 gathered papers per review, and report generation covering up to 135 papers. This is the optimal tier for university faculty, PhD candidates, and clinical research teams conducting formal systematic reviews.
• Scale & Enterprise: Expands gathered paper limits to 20,000–40,000 papers, enables multi-reviewer Dual Review screening with Cohen's kappa tracking, allows data extraction from figures/charts, and includes centralized billing and API access.

Managing Usage and Credits

Premium PDF parsing processes documents through heavier multimodal architectures. Researchers should leave Premium parsing turned off for clean, text-heavy PDFs and selectively enable it when dealing with scanned documents, dense two-column clinical trials, or extensive statistical tables to optimize monthly usage allowances.

Best Practices for Academic Rigor and PRISMA Compliance

While Elicit automates repetitive transcription, scientific integrity requires adherence to methodological safeguards:

• Never skip manual spot-checks: While Elicit reduces extraction time by up to 80%, ambiguous reporting in original papers (such as inconsistent sample sizes reported between abstracts and text) can occasionally lead to misinterpretations. Always click cell quotes to confirm primary numbers.
• Document AI usage transparently: When writing PRISMA methodology sections, report Elicit's role transparently. Specify the exact prompts used for custom extraction columns, the date of query execution, and the dual-adjudication procedure used by human authors.
• Maintain dual-reviewer verification: Cochrane and PRISMA standards recommend independent dual extraction. Using Elicit as an initial automated extractor followed by two human verifiers significantly accelerates timelines while satisfying peer-review scrutiny.

References

• Home | Elicit Help Center
• Systematic reviews | Elicit Help Center
• Elicit tips and best practices | Elicit Help Center
• Create and save columns in Elicit | Elicit Help Center
• Improve column results | Elicit Help Center
• Extracting data from a table or figure within a paper in column answers | Elicit Help Center
• Export your data from Elicit | Elicit Help Center
• Systematic Reviews in Elicit | Elicit Help Center