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Record W3197851918 · doi:10.1136/bmj.n2126

Elimination of covid-19: beware of surveillance bias

2021· article· en· W3197851918 on OpenAlexaff
Stefano Tancredi, Daniela Anker, Laura C. Rosella, Arnaud Chioléro

Bibliographic record

VenueBMJ · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsMcGill UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)CLARITYPandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)HappeningModality (human–computer interaction)MedicineStatisticsVirologyMathematicsComputer scienceHistoryInternal medicineOutbreakArtificial intelligenceDiseaseBiology

Abstract

fetched live from OpenAlex

Surveillance bias occurs when looking at health conditions that have differential intensity across populations, over time, or according to care setting or type of patient. 1 As a result, any difference in frequency of the condition might not reflect a change in the actual risk of this condition but rather differences in the modality of detection or patient characteristics.

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)0.414
metaresearch head score (Gemma)0.757
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.586
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4140.757
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0080.009
Science and technology studies0.0020.003
Scholarly communication0.0070.006
Open science0.0050.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0190.007

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 head0.753
GPT teacher head0.620
Teacher spread0.133 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations13
Published2021
Admission routes1
Has abstractyes

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