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Record W2111194470 · doi:10.1503/cmaj.150391

Reducing pain during vaccine injections: clinical practice guideline

2015· article· en· W2111194470 on OpenAlexafffundvenue
Anna Taddio, C. Meghan McMurtry, Vibhuti Shah, Rebecca Pillai Riddell, Christine T. Chambers, Mélanie Noël, Noni E. MacDonald, Jess Rogers, Lucie M. Bucci, Patricia Mousmanis, Eddy Lang, Scott A. Halperin, Susan K. Bowles, Christine Halpert, Moshe Ipp, Gordon J. G. Asmundson, Michael Rieder, Kate Robson, Elizabeth Uleryk, Martin M. Antony, Vinita Dubey, Anita Hanrahan, Donna Lockett, J. Anthony G. Scott, Elizabeth Votta Bleeker

Bibliographic record

VenueCanadian Medical Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicIntramuscular injections and effects
Canadian institutionsCanadian Psychological AssociationToronto Metropolitan UniversityCanadian Respiratory Research NetworkBC Centre for Disease ControlCalgary Laboratory ServicesCanadian Wood CouncilAlberta Health ServicesSickKids FoundationYork UniversityCanadian Public Health AssociationUniversity of CalgaryMount Sinai HospitalInstitute for Work & HealthIzaak Walton Killam Health CentreChildren’s Health Research InstituteHospital for Sick ChildrenDalhousie UniversityUniversity of TorontoWestern University
FundersBritish Columbia Centre for Disease ControlCanadian Institutes of Health ResearchCanadian Psychological AssociationAssociation des pharmaciens du CanadaCollege of Family Physicians of CanadaOntario Ministry of Health and Long-Term CareMAYDAY Fund
KeywordsMedicineVaccinationGuidelineImmunizationOutbreakIntensive care medicineImmunologyVirologyPathologyAntibody

Abstract

fetched live from OpenAlex

Pain from vaccine injections is common, and concerns about pain contribute to vaccine hesitancy across the lifespan.[1][1],[2][2] Noncompliance with vaccination compromises the individual and community benefits of immunization by contributing to outbreaks of vaccine-preventable diseases. Individuals

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)0.012
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0120.008

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 head0.018
GPT teacher head0.337
Teacher spread0.319 · 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

Citations284
Published2015
Admission routes3
Has abstractyes

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