AI models during Data extraction (from text)
While setting up Data Extraction form, we can add AI models to the various fields.
Laser AI suggestions are extracted directly into the extraction form. There is a connection between extracted value and PDF.
1. Click on the AI suggestion in the data extraction form
2. Check if the underlined text is the correct extracted value
3. Accept or reject the model suggestion
Models are continuously updated - the list of currently available models is presented below.
Model name | Cooperative models* | Type of SRs/topic | Description |
Models that support extraction of study characteristics | |||
Study identifier | - | Universal | Unique code used to identify research work, like NCT number |
Study name | - | Universal | Refers to the trial name, added by the authors of the study, e.g. coming from the first letters of the study name |
Study objective | - | Universal | Extracts the study objective. |
Study conclusion | - | Universal | Extracts the main conclusions of the study |
Study limitations | - | Universal | Extracts the study limitations as reported by the study authors |
Country name | - | Universal | Extracts countries where the study was conducted |
Was this study multi-center or single-center | - | Standard/human | Defines if the study was single- or multicenter |
Study design e.g. RCT, prospective, observational, cross-sectional etc | - | Universal | Refers to the methodologies used in research to gather the data needed to explore a specific question, e.g. observational or randomized-controlled trial. |
Study design comment | - | Universal | More detailed information about study design |
Study phase e.g I, II, III, IV | - | Standard/human | Describes the clinical trial phase (I-IV) |
Statistical methodology | - | Standard/human | Describes the statistical methodology which was used in the study |
Efficacy analysis type | - | Standard/human | Defines the type of efficacy analysis - ITT, mITT, per protocol |
Safety analysis type | - | Standard/human | Defines the type of safety outcomes analysis - ITT, mITT, per protocol |
Cross-over design (yes/no) | - | Standard/human | Specifies whether crossover design was used in the study |
Data reported for cross-over | - | Standard/human | Was the data reported separately for the cross-over? (yes or no) |
Study funding/sponsor information | - | Universal | Funder or sponsor of the study |
Study period | - | Universal | Describes study duration (range of years, as reported in the study) |
Timepoint (follow-up length) | - | Universal | One instance for every time point/follow-up length reported in the study |
Support critical appraisal | |||
Validity of statistical analysis | - | Standard/human | Did the analysis include an intention-to-treat analysis? If so, was this appropriate and were appropriate methods used to account for missing data? Recommended for ROB Domain 3: Missing outcome data SQ [D3.1] Were data for this outcome available for all, or nearly all, participants randomised? |
Validity of outcome reporting | - | Standard/human | Is there any evidence suggesting that the authors measured more outcomes than reported? Recommended for Domain 5: Risk of bias in the selection of the reported result SQ [5.1] Were the data that produced this result analysed following a pre-specified analysis plan finalized before unblinded outcome data were available for analysis? |
Validity of withdrawals | - | Standard/human | Were there any unexpected imbalances in drop-outs between groups? If so, were they explained or adjusted for? Recommended for ROB Domain 3: Missing outcome data SQ [D3.1] Were data for this outcome available for all, or nearly all, participants randomised? |
Validity of baseline characteristics | - | Standard/human | Were the groups similar at the outset of the study in terms of prognostic factors, for example, severity of disease? Recommended for ROB: Domain 1: Risk of bias arising from the randomization process SQ [D1.3] Did baseline differences between intervention groups suggest a problem with the randomization process? |
Study allocation blinding e.g. open-label, single, double, triple-blind | - | Standard/human | Were the care providers, participants and outcome assessors blind to treatment allocation? If any of these people were not blinded, what might be the likely impact on the risk of bias (for each outcome)?) Recommended for ROB Domain 2: Risk of bias due to deviations from the intended interventions SQ [D2.1] Were participants aware of their assigned intervention during the trial? SQ [D2.2] Were carers and people delivering the interventions aware of participants' assigned intervention during the trial? |
Study assessment blinding (yes, no) | - | Standard/human | Recommended for ROB Domain 2: Risk of bias due to deviations from the intended interventions SQ [D2.1] Were participants aware of their assigned intervention during the trial? SQ [D2.2] Were carers and people delivering the interventions aware of participants' assigned intervention during the trial? |
Study allocation concealment | - | Standard/human | Was randomisation carried out appropriately? Recommended for ROB: Domain 1: Risk of bias arising from the randomization process |
Models that support extraction of concepts related to study populations | |||
Study population | - | Standard/human | Extracts data related to the study population, such as disease. |
Inclusion criterion | - | Universal |
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Exclusion criterion | - | Universal |
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Number of patients screened | - | Standard/human | Extract the number of patients screened for eligibility before the study begins. |
Number of patients included | - | Standard/human | Extracts the number of patients who were randomized. To extract the number of patients included in each study group, please use the model: ‘Study arm: Number of patients in the study arm’. |
Number of patients that completed the study | - | Standard/human | Extracts the number of patients who completed the study. |
Previous therapy details | - | Standard/human | Refers to the information if any prior therapies were used |
Subgroup: Name of the subgroup | - | Standard/human | Extracts subgroups that were evaluated in the study |
Baseline characteristic: name of the baseline characteristic | Yes | Standard/human | Extracts all the variable names that are included in the baseline characteristics (e.g. sex, age, Body mass index, region etc) |
Baseline characteristic: Unit | Yes | Standard/human | Extracts the units of available baseline variables (e.g. centimetres for height) |
Baseline characteristic: Central tendency type | Yes | Standard/human | Extracts the available type of the central tendency (e.g. mean, median etc.) for the arm comparison |
Baseline characteristic: Central tendency value | Yes | Standard/human | Extracts the central tendency value for the arm comparison |
Baseline characteristic: Variance type | Yes | Standard/human | Extracts the available type of variance (e.g. range, IQR, SE etc.) for the arm comparison |
Baseline characteristic: Variance value | Yes | Standard/human | Extracts the variance value for the arm comparison |
Models that support extraction of concepts related to interventions/study arms | |||
Study arm: Name of the intervention | Yes | Standard/human | Extracts the detailed name of the intervention |
Study arm: Dose of the intervention | Yes | Standard/human | Extracts the dose for each of the study arms |
Study arm: Unit of the dose of the intervention | Yes | Standard/human | Extracts the dose unit for each of the study arms |
Study arm: Route of administration of the intervention | Yes | Standard/human | Extracts the route of administration for each of the study arms |
Study arm: Frequency of administration of the intervention | Yes | Standard/human | Extracts the frequency of administration for each of the study arms |
Study arm: Name of the study arm | Yes | Standard/human | Extracts the names of each of the study arms |
Study arm: Number of patients in the study arm | Yes | Standard/human | Extracts the number of patients who were randomized in each of the study arms. |
Treatment time | - | Standard/human | Duration of treatment, e.g. 10 days and details on when the treatment was discontinued, e.g. 10 days or until discharge |
Concomitant treatment | - | Standard/human | Concomitant treatment administered along with the studied intervention |
Models that support extraction of outcome data | |||
Outcome: Name of the outcome | Yes | Standard/human | Extracts the name of the outcome from the article |
Outcome: What was the outcome measured with | Yes | Standard/human | How was the outcome measured? E.g. questionnaire (specify its name), blood test, etc. |
Outcome: Unit | Yes | Standard/human | Extracts the outcome unit (e.g. points, cm, etc) |
Outcome: Timepoint (follow-up length) | Yes | Standard/human | Extracts the specific time point or window when authors collected data for the outcome |
Models that support extraction of outcome data - Comparison level | |||
Outcome: Effect measure | Yes | Standard/human | Extracts the effect measure type (e.g. define if it’s OR/RR/HR etc) for the arm comparison |
Outcome: Effect measure value | Yes | Standard/human | Extracts the value of effect measure (e.g. the value for OR/RR/HR etc) for the arm comparison |
Outcome: p-value | Yes | Standard/human | Extracts the p-value (for the specific outcome) for the arm comparison |
Outcome: Variance type | Yes | Standard/human | Extracts the type of the variance (e.g. define if it’s 95%CI, range etc) for the arm comparison |
Outcome: Variance value | Yes | Standard/human | Extracts the value of variance (e.g. value for 95%CI) for the arm comparison |
Models that support extraction of outcome data - Arm level | |||
Outcome: Central tendency type | Yes | Standard/human | Extracts the available type of the central tendency (e.g. mean, median etc.) for the arm comparison for the outcomes |
Outcome-Study arm: value | Yes | Standard/human | Extracts the outcome value for each of the study arms |
*Several models cooperate during the data extraction process. Within Laser AI, you will find three types of combined models.
Models supporting outcome data extraction
Models supporting baseline data extraction
and models supporting intervention extraction
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