Top Tweets for #PROBAST
After 3 y following the #PROBAST and #TRIPOD guidelines, I can’t remember many editors or reviewers that appreciate it.
Not to mention transparent and reproducible @ReproducibiliT analysis, including preregistered protocols.
Here things I remember what matters for them:
1/
My first tweet for my first preprint! 🎉
Systematic review and meta-analysis of external validations of ADNEX model for ovarian cancer diagnosis.
Including critical appraisal with @TRIPODStatement and #PROBAST.
https://t.co/c4lAeYbCZ8
"We need tools to evaluate research output to ensure scientific validity, clarity of presented results, reproducibility, and adherence to ethical standards." https://t.co/DpiMie8zDF @Klonmich #PROBAST #MachineLearning #AI
"We need tools to evaluate research output to ensure scientific validity, clarity of presented results, reproducibility, and adherence to ethical standards." https://t.co/DpiMie8zDF @Klonmich #DeepLearning #STARD #PROBAST
Guidelines for Reporting #AI Research https://t.co/DpiMie8zDF @Klonmich #PROBAST #MachineLearning #ML
If you're curious to know more about what we look for when assessing a prediction model, the #TRIPOD and #PROBAST tools from @EQUATORNetwork are a great place to start 👇 [3/3] @avid_podcast @LetsTalkSciComm
https://t.co/ezmLEWrqp8
https://t.co/GX32KDkPwm
@Rad_Nation @LabFuller @TRIPODStatement @EQUATORNetwork T2. And the newly minted #TRIPOD #AI and #PROBAST #AI guideline are even more #AI oriented. Thanks @Rad_Nation and @ianjpereira for the heads up 😊 https://t.co/oewzl3kMJr #radonce #jc 3/n
@yodohtt Hello there is your unroll: Thread by @Michael_Harhay: "May I crowdsource a prediction / ? @MaartenvSmeden @GSCollins. If research team uses a large Delphi approach to […]" #PROBAST https://t.co/FIhjrIh4jq
Enjoy :) 🤖
But there were a few common concerns about these clearances. Some did not improve anything more than AUC or “detection” rates. Some were not compared to anything. Many did not adhere to established guidelines for predictive models (e.g. @TRIPODStatement, #PROBAST)
Our newest publication - introducing #PROBAST as a brand new tool for assessing risk of bias and applicability in prediction model studies - is out now! https://t.co/MZ5M8TerV7 via @AnnalsofIM @UniUtrecht @KeeleUniversity H/T @wolffrf @CarlMoons @JosKleijnen @hans_reitsma #OA
Doing a systematic review of prediction models? Use PROBAST for quality assessment (risk of bias) of identified studies - our publications are out: https://t.co/Ok3XMpyH4Z with explanation https://t.co/VPLrXu7ydz @wolffrf @CarlMoons @GSCollins @JosKleijnen @hans_reitsma #PROBAST
More information on #PROBAST, including the tool and examples, can be found on https://t.co/hnsGbyW5Ej
Finally! The long awaited Risk of Bias tool for prediction models PROBAST has been published!
@wolffrf, @CarlMoons, @Richard_D_Riley, Penny #Whiting, Marie #Westwood, @GSCollins, Johannes #Reitsma, @JosKleijnen, and Sue #Mallett for the #PROBAST group in @AnnalsofIM: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies https://t.co/etE1ShLUEh
Our KSR colleague @wolffrf 's presentation on #PROBAST drew a big crowd! Next stop, Wembley Stadium! For more info on #PROBAST: #Riskofbias tool for prediction modelling studies, see https://t.co/FzgT3eMr0k
#riskofbias at #CochraneForAll ǀ @cochranecollab Colloquium 2018 on Tuesday (3/4): Assessing the methodological strengths and limitations of qualitative evidence: what are the key criteria?, 14:00-15:30, Tinto, https://t.co/ov92k5E2AB
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