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Association of SARS-CoV-2 infection with neurological impairments in pediatric population: A systematic review

2023· review· en· W4389569325 on OpenAlexaff
Angela T.H. Kwan, Jacob S. Portnoff, Khaled Al-Kassimi, Gurkaran Singh, Mehrshad Hanafimosalman, Marija Tesla, Nima Gharibi, Tiffany Lasky, Ziji Guo, Davaine Joel Ndongo Sonfack, Julia Martyniuk, Saman Arfaie, Mohammad Sadegh Mashayekhi, Mohammad Mofatteh, Richie Jeremian, Kevin Ho, Luis Rafael Moscote‐Salazar, Ángel Lee, Muhammad Youshay Jawad, Felicia Ceban, Kayla M. Teopiz, Rodrigo B. Mansur, Roger Ho, Joshua D. Rosenblat, Bing Cao, Taeho Greg Rhee, Roger S. McIntyre

Bibliographic record

VenueJournal of Psychiatric Research · 2023
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsMcGill UniversityUniversité LavalBrain and Cognition Discovery FoundationMcGill University Health CentreUniversity of OttawaUniversity of British ColumbiaToronto Metropolitan UniversityUniversity of TorontoUniversity Health Network
FundersNational Institute of Mental HealthNational Institute on Aging
KeywordsMedicinePediatricsIrritabilityPopulationMEDLINECochrane LibraryObservational studyNeurocognitiveCINAHLCohort studyPsychiatryInternal medicineAnxietyMeta-analysisCognitionPsychological intervention

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)0.002
metaresearch head score (Gemma)0.011
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.118
GPT teacher head0.487
Teacher spread0.368 · 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

Citations6
Published2023
Admission routes1
Has abstractno

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