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A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

2021· review· en· W3121796306 on OpenAlexaff
Jasjit S. Suri, Sushant Agarwal, Suneet Gupta, Anudeep Puvvula, Mainak Biswas, Luca Saba, Arindam Bit, Gopal S. Tandel, Mohit Agarwal, Anubhav Patrick, Gavino Faa, Inder M. Singh, Ronald Oberleitner, Monika Turk, Paramjit S. Chadha, Amer M. Johri, João Sanches, Narendra N. Khanna, Klaudija Višković, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, David Sobel, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Puneet Ahluwalia, Jagjit S. Teji, Mustafa Al-Maini, Surinder Dhanjil, Meyypan Sockalingam, Ajit Kumar Saxena, Andrew Nicolaides, Aditya Sharma, Vijay Rathore, J N Ajuluchukwu, Mostafa Fatemi, Azra Alizad, Vijay Viswanathan, Pranav Krishnan, Subbaram Naidu

Bibliographic record

VenueComputers in Biology and Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 diagnosis using AI
Canadian institutionsAllerGenQueen's University
FundersNational Institute of Mental Health
KeywordsARDSMedicineCoronavirus disease 2019 (COVID-19)Acute respiratory distressIntensive care medicineNoveltyIdentification (biology)PneumoniaModalitiesPandemicLungPathologyPsychologyInternal medicine

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.124
GPT teacher head0.462
Teacher spread0.338 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations69
Published2021
Admission routes1
Has abstractno

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