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Record W4283377515 · doi:10.14785/lymphosign-2022-0003

2022 Canadian resource guides for individuals and families affected by primary immunodeficiency

2022· article· en· W4283377515 on OpenAlexafffundvenueabout
Wendy Shama

Bibliographic record

VenueLymphoSign Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersTakeda CanadaGrifolsIWK Health CentreImmunodeficiency CanadaBC Children's Hospital
KeywordsPrimary immunodeficiencyNoveltyResource (disambiguation)Primary careImmunodeficiencyMedicineWork (physics)Family medicinePsychologyComputer scienceImmunologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

A diagnosis of immunodeficiency can be challenging for families as they navigate the emotional impact of this diagnosis, as well the potential financial burden of treatment. As is the case with many rare diseases, there existed a paucity of information for families looking for appropriate resources related to their diagnosis. The Primary Immunodeficiency Social Work Network was established in 2011 by Immunodeficiency Canada to develop a network of social workers across Canada who work with patients diagnosed with primary immunodeficiency. This network has had a focus on support programs, education, and research. Resource guides were created by the network with the goal of providing comprehensive support and information on resources available for families and individuals affected by primary immunodeficiency in each province as well as those available nationally. Statement of Novelty: National and provincial resources guides, reviewed and updated yearly, have been created for families and individuals affected by primary immunodeficiency.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.161
Threshold uncertainty score0.540

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1610.050

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 head0.006
GPT teacher head0.219
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreOther

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 routes4
Has abstractyes

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