FAQ
Frequently asked questions about biologit MLM-AI
Last updated
Frequently asked questions about biologit MLM-AI
Last updated
Newly generated reviews may present pre-screened abstracts that were tagged at the stage. Duplicate abstracts relate to a corresponding abstract in the same review, or in a previous review for the same product.
Reviews always use the monitor settings in effect at the time of submission. This means any monitor changes performed later will not be reflected. This is by design to ensure traceability: the system behaves and reflects monitor settings current at the time the review was created.
Note the same applies to scheduled reviews: once a review is scheduled (i.e. it appears in the "" tab), the settings are fixed.
MLM-AI performs duplicate detection by ID, DOI and content. This is further detailed in the page:
Articles are flagged as duplicates only when:
They appear in the same review results. One of the articles will be automatically reviewed as "duplicate".
An article is a duplicate of another article seen in a past review for the same Monitor.
Reviews stay "In Progress" while there are still abstracts to be screened, and move to "Completed" once all abstracts have been screened ie. all abstracts were saved with a decision by a user or by MLM-AI via automated action.
Search results obtained from PubMed, EMBASE, EBSCO, Adis, and ProQuest can be directly uploaded into MLM-AI for screening.
The supported formats and help with the export steps are detailed in:
The query syntax of the MLM-AI database allows for wildcards that can expand common suffixes, for example:
toxic* -> toxic, toxicity, toxicities
pregna* -> pregnant, pregnancy, pregnancies
In certain cases the - is ignored to facilitate matching of commonly (non) hyphenated terms like "where by" and "where-by"
Yes, a maximum of 1000 hits can be retrieved from a single query. Try reducing the date range or refining the search criteria.
MLM-AI ingests data from established and open access databases to achieve a broad literature reach. Currently, we automatically scan and upload the following sources:
Note: As of May/2024 the IBECS source is no longer being updated with new publications at the source
There are two options:
In the example below, the key terms associated with "etanercept" are OR'ed together, and searched across all sources specified in the monitor.
Including more repositories will broaden the reach of scientific literature available to you, and can contribute in identifying more relevant safety information.
Additional databases can also help in meeting your regulatory requirements.
See also these resources for further information on this topic:
MLM-AI models are product agnostic, having been trained with a cross-section of scientific literature covering a broad range of abstracts.
All MLM-AI models are configured for high recall on the target category of interest (Suspected AE, Suspected Case etc). This means AI tags produced by MLM-AI are "conservative" in that it tolerates some amount of false positives so that missing a relevant abstract is highly unlikely.
For performance results please refer to the latest fact sheet:
Title-only citations may sometimes appear, depending on how journals decide to publish their data.
Without an abstract there is insufficient information to reliably make model predictions
Hence, all "No Abstract" articles are tagged as "Suspected AE", indicating they should be screened
MLM-AI models were designed so that relevant safety information is not missed. With this in mind, we have labeled and curated datasets that capture how likely safety information is present either in the abstract or in the full text of an article.
Consider the samples below extracted from abstracts. The most ambiguous cases may still be relevant, and will likely need review of the full text of the article:
By looking for "suspected" safety information, MLM-AI can still tag abstracts with incomplete information without missing important safety data.
See our article discussing the the use of AI for literature screening:
See also the model technical specification and intended uses in the FactSheet:
Finally, our technical paper goes in more details on our AI development methodology and experimental results:
Any modification to monitors apply only to Reviews created after the change.
Existing reviews always use the monitor configuration present at the time of review creation. This ensures consistent behavior with the then current settings. This also applies to "Scheduled" reviews that have been submitted and are already in the "Processing" tab.
Maximum number of synonyms used in a monitor: 600
Maximum configured monitors per account: 100
Maximum configured special situation options: 50
Maximum configured exclusion options: 50
Workflow
Maximum configurable workflow decisions (system settings or monitor): 50
Maximum number of teams assigned to a single user: 10
Maximum configured teams per account: 50
Maximum number of days in a review: 95
Oldest start date of a review (number of days from today): 450
Date where data starts in the MLM-AI article database: 01-JAN-2020
Attachments
Maximum size of uploaded attachment: 50Mb
Maximum results in a request: 1000
By default every user is subject to the following password rules:
Minimum password length: 10 characters
At least 2 numbers in password
At least 1 lowercase letter
At least 1 uppercase letter
At least 1 special character ($&?, etc...)
See also the for more details
Duplicate detection is a feature of and is active only when Reviews are requested. Note that screen, and benefit from duplicates detection this way.
Yes, once are configured for your system, users for articles that qualify for ICSR.
Quality control with is supported. Users can also to clearly indicate QC status.
Finally, if required users can directly assign articles for QC using the .
- Comprehensive repository of biomedical and life sciences literature
- Worldwide open access repository of scientific literature
The (DOAJ) - Worldwide Open Access literature
- Scientific life sciences literature focusing on Latin America and the Caribbean
- open access repository focused on literature from Spain
- open access repositories with regional coverage in Spain, Latin America and the Caribbean
(1) When creating a monitor using product name and synonyms, MLM-AI will match any of the key terms specified in the .
(2) Monitors can also be created from a search string created by the user. From , create a query string to suit your needs and proceed to create a monitor using the "pill" button. The monitor query string is exactly the one created by the user.
There are various strategies that can be employed for the safe implementation of AI-based screening. In we discuss techniques to leverage AI into your workflows.
There are many ways in which users can take advantage of AI Tags produced by MLM-AI. Please see the section on to learn more and use one that best suits your needs.
The best resource on how to safely integrate AI tags is our .
"No Abstract" citations are also presented in in Review results, to facilitate a separate screening workflow
Medication synonyms presented in the pages are periodically (at least quarterly) collected from the following public sources: , , and the .
Note: this limit does not apply to .
Yes, to request different password rules for your account.
Password auto-lock prevents account break-in attempts by temporarily locking the account after too many failed logins. This is a system configuration that administrators can setup via the Settings page. Learn more more on .
