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TRUVACE RECORD VERSION
record: TRV-2026-2818
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:28:45.401394Z
status: published
lens: trace
sector: science
headline: Decoding semi-automated title-abstract screening: findings from a convenience sample of reviews
dek: BACKGROUND: We evaluated the benefits and risks of using the Abstrackr machine learning (ML) tool to semi-automate title-abstract screening and explored whether Abstrackr's predictions varied by review or study-level characteristics. METHODS: For a convenience sample of 16 reviews for which adequate data were available to address our objectives (11 systematic reviews and 5 rapid reviews), we screened a 200-record training set in Abstrackr and downloaded the relevance (relevant or irrelevant) of the remaining rec…
gain_title: In a retrospective simulation on 16 evidence reviews, using Abstrackr's predictions with liberal-accelerated screening saved a median 26 hours of title-abstract screening per review (35% of records) compared with dual independent screening.
problem_title: In the same 16-review simulation of Abstrackr, the tool wrongly excluded 0 to 3 studies per review (0 to 14% of those included), and 13% of 802 included studies were predicted irrelevant, which changed one meta-analysis from significant to non-significant.
trace_subject: Abstrackr machine learning-assisted title-abstract screening in systematic and rapid reviews
gain_reading: In a retrospective simulation on 16 evidence reviews, using Abstrackr's predictions with liberal-accelerated screening saved a median 26 hours of title-abstract screening per review (35% of records) compared with dual independent screening.
gain_evidence: saved a median (IQR) 26 (9, 42) h of screening time | The median (IQR) workload savings across reviews was 3143 (1044, 5023) records (35 (30, 43) %)
problem_reading: In the same 16-review simulation of Abstrackr, the tool wrongly excluded 0 to 3 studies per review (0 to 14% of those included), and 13% of 802 included studies were predicted irrelevant, which changed one meta-analysis from significant to non-significant.
problem_evidence: we wrongly excluded 0 to 3 (0 to 14%) records that were included in the final reports | The effect was no longer statistically significant following the removal of McCracken et al. from the analysis
quick_read: Researchers at an evidence synthesis centre tested Abstrackr, a free machine learning tool, on 16 of their own reviews (11 systematic, 5 rapid). After a reviewer screened 200 training records, they simulated letting the tool's predictions replace part of the second reviewer's work. The simulation saved a median 26 hours of screening per review and wrongly excluded 0 to 3 studies per review.

Time savings matter because title-abstract screening is one of the slowest steps in producing a systematic review. The tool correctly flagged 87% of the 802 studies that ended up in the final reports, and in six of ten systematic reviews it missed fewer studies than a single human reviewer would have.

The risks are uneven. Predictions were more accurate for trials (91%) than observational studies (79%) or reviews (83%), and only one of eight affected meta-analyses changed significance. The authors call several findings paradoxical and say prospective studies are needed.
limitation: The 16 reviews were a convenience sample, the protocol was not published in advance, only one tool and one screening approach were tested, and only eight meta-analyses in one review were affected by missed studies, so the authors caution against generalising.
tag: Dual reading
key_points: The sample was 16 reviews from one centre, 11 systematic reviews and 5 rapid reviews, chosen because adequate data were available. | A 200-record training set was screened in Abstrackr, and its hard relevant/irrelevant predictions were used to simulate liberal-accelerated screening against dual independent screening. | Median workload savings were 3,143 records (35%), equal to 26 hours or about 3 working days; no studies were missed in 5 of 10 systematic reviews. | Of 802 included studies, 696 (87%) were correctly predicted relevant and 106 (13%) were wrongly predicted irrelevant. | Accuracy was 95% in reviews of only trials versus 86% in reviews with multiple designs, and 89% with multiple research questions versus 83% with a single question. | One missed antipsychotic trial fed eight meta-analyses; only the compulsions result changed, from a significant reduction (MD -1.53) to a non-significant one (MD -1.17). | The simulation beat single-reviewer screening in 6 of 10 systematic reviews, for example 1% versus 11% missed in the activity and pregnancy review.
rundown: Records were screened in Abstrackr using random order and a single-reviewer setting, replicating the senior reviewer's original decisions for the 200-record training set. Predictions usually arrived within 24 hours. Time savings were estimated at 0.5 minutes per record and an 8-hour day.

In the simulation, the senior reviewer screened only records predicted relevant and the second reviewer checked only those excluded by either the senior reviewer or the tool. Half of the systematic reviews lost no studies, two lost one record (1%) and three lost three records (2 to 14%). A missed Chinese-language study with an English abstract was one of them.

Of 49 antipsychotic pairwise comparisons with at least low-strength evidence, one missed study appeared in 8 (16%), and only one changed statistical significance. The authors say removing missed studies would not have changed the main conclusions, and that comprehensive searches would likely catch such studies by other means.

Accuracy differed by study traits: trials 91%, qualitative studies 93%, observational studies 79%. Studies at unclear or high risk of bias were predicted correctly more often (88%) than low-risk-of-bias studies (80%), and the authors call this and other findings paradoxical. The approach saved slightly more time than a semi-automated second-screener method (20 hours versus 26) with identical missed records.
sources:
- peer_reviewed | Systematic Reviews | https://doi.org/10.1186/s13643-020-01528-x | 2020-11-26
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