Evidence map›Paper›PMID 38523781›Full record

ArticleiScience2024

Analysis and prediction in SCR experiments using GPT-4 with an effective chain-of-thought prompting strategy.

Muyu Lu, Fengyu Gao, Xiaolong Tang, Linjiang Chen

Abstract read
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Muyu LuSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China.
Fengyu GaoSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China.
Xiaolong TangSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China.
Linjiang ChenSchool of Chemistry and School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human-like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NO

Indexed as

ChemistryComputer scienceNatural sciences

Identifiers

PMID38523781
PMCPMC10960113

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.