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Record W4403692526 · doi:10.21203/rs.3.rs-5208212/v1

Common Variable Immunodeficiency Disorder: A Decade of Insights from a Cohort of 150 Patients in India and the Use of Machine Learning Algorithms to Predict Severity

2024· preprint· en· W4403692526 on OpenAlexaff
Umair Ahmed Bargir, Priyanka Setia, Mukesh Desai, Chandrakala Shanmukhaiah, Aparna Dalvi, Shweta Shinde, Maya Gupta, Neha Jodhawat, Amrutha Jose, Mayuri Goriwale, Reetika Malik Yadav, Disha Vedpathak, Lavina Temkar, Snehal Shabrish, Gouri Hule, Vijaya Gowri, Prasad Taur, Amita Athawale, Farah Jijina, S. Bhatia, Akash Shukla, Manas Kalra, Meena Sivasankaran, Sarath Balaji, Punit Jain, Sujata Sharma, Harikrishnan Gangadharan, Gaurav Narula, Ratna Sharma, Pranoti Kini, Mamta Mangalani, Abhishek Zanwar, Himanshi Chaudhary, Narendra Kumar Chaudhary, Ujjawal Khurana, Ashish Bavdekar, Girish Subramaniam, Revathi Raj, Subhaprakash Saniyal, Nitin Shah, Tehsin Petiwala, Prawin Kumar, Venkatesh S Pai, Sagar Bhattad, Abhinav Sengupta, Manish Soneja, Dayanand Upase, Abhijeet Ganapule, Manisha Madkaikar

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsASTER
Fundersnot available
KeywordsCohortVariable (mathematics)AlgorithmCommon variable immunodeficiencyArtificial intelligenceMachine learningPsychologyComputer scienceMedicineMathematicsInternal medicineImmunology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.311
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractno

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