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Additional file 1 of Physician vaccination practices in mild to moderate inborn errors of immunity and retrospective review of vaccine completeness in IEI: results from the Canadian Immunization Research Network

2022· article· en· W6920705132 on OpenAlexaffabout

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsDalhousie UniversityMcMaster UniversityCentre Hospitalier Universitaire Sainte-JustineUniversité LavalUniversity of SaskatchewanUniversity of British ColumbiaIzaak Walton Killam Health CentreUniversity of OttawaPublic Health Agency of Canada
Fundersnot available
KeywordsTable (database)VaccinationImmunizationCompleteness (order theory)Retrospective cohort studyImmunityInfluenza vaccine

Abstract

fetched live from OpenAlex

Additional file 1: Table S1. IDS (N=19) versus Immunologists (N=23) perceptions of vaccine effectiveness and safety. Table S2. Correlation of Physicians Concerns and Management Approach. Table S3. Specific Immune Deficiencies among Participants in the Retrospective Review. Table S4. Influenza Vaccine Completeness at Age 7. Table S5. Lymphocyte subset values prior live immunization, N=22. Table S6. Serology Studies Sent Post Vaccination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6270.036

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 head0.064
GPT teacher head0.309
Teacher spread0.245 · 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.

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
Published2022
Admission routes2
Has abstractyes

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