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Record W2145074183 · doi:10.12927/hcpol.2013.23398

The Rising Prevalence of Asthma: True Increase, Diagnostic Exchange or Diagnostic Accuracy?

2013· article· fr· W2145074183 on OpenAlexafffundvenueabout
Randy Fransoo, Patricia J. Martens, The Need To Know Team, Heather J. Prior, Alan Katz, Nathan Nickel, Dan Château

Bibliographic record

VenueHealthcare policy · 2013
Typearticle
Languagefr
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of ManitobaManitoba Health
FundersUniversity of Manitoba
KeywordsAsthmaMedicineEnvironmental healthBronchitisDiagnostic testDiagnostic accuracyChronic bronchitisDemographyPediatricsInternal medicine

Abstract

fetched live from OpenAlex

In the midst of frequent reports about "the asthma epidemic," results from a number of studies by the Manitoba Centre for Health Policy have shown stable or decreasing prevalence of an overall indicator of respiratory diseases which includes asthma.To resolve these apparently contrary findings, we conducted a time trend analysis using administrative data.Results revealed significant potential for diagnostic exchange: asthma prevalence increased, but that of bronchitis decreased. RésuméDans le contexte des fréquents rapports indiquant une « épidémie d' asthme », les résultats de plusieurs études effectuées par le Centre des politiques de santé du Manitoba montrent une prévalence stable ou en déclin d'un indicateur global des maladies respiratoires, y compris l' asthme.Afin de résoudre la contradiction apparente de ces résultats, nous avons effectué une analyse évolutive des tendances au moyen de données administratives.Les résultats révèlent une forte possibilité de substitution de diagnostic : la prévalence de l' asthme a augmenté, mais celle de la bronchite a diminué.

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)0.064
metaresearch head score (Gemma)0.225
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.225
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.345
Teacher spread0.318 · 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 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

Citations9
Published2013
Admission routes4
Has abstractyes

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