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Record W4404807455 · doi:10.1370/afm.22.s1.6817

Primary care severe asthma registry project-phase 1: e-Delphi results for registry entry and indices of clinician behaviour

2024· article· en· W4404807455 on OpenAlexaboutno aff
Ijeoma Itanyi, Katrina D’Urzo, Gurnoor Brar, Karen Tu, Kenneth R. Chapman, Susan Waserman, Tony D’Urzo

Bibliographic record

VenueThe Annals of Family Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaPrimary careMedicinePatient registryDelphi methodDelphiFamily medicinePediatricsComputer scienceInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Context: A severe asthma registry in primary care is needed to determine the population prevalence and best practices in the real world. Objective: To establish consensus on a) a definition of severe asthma, b) criteria for severe asthma registry entry and c)indices of clinician behaviour. Study Design and Analysis: We conducted an e-Delphi panel in four rounds. We used open-ended survey in Round 1 to identify potential items to be considered as criteria for consensus items (a, b and c) above. A 5-point Likert scale from strongly disagree to strongly agree was used for voting on these items in Rounds 2 and 3. Criteria which had ≥80% votes on neutral, agree or strongly agree entered the next round. Consensus criteria were established at the end of Round 3. In Round 4, panelists categorized these criteria as core, desired or optional data elements. Setting or Dataset: The University of Toronto Practice-based and Learning Research Network (UPLEARN) in Ontario, Canada, comprises ~400 primary care physicians contributing data on ~400,000 patients. Population Studied: Family physicians, respirologists, allergists, respiratory therapists, and respiratory researchers from within and outside of UPLEARN were purposively sampled to participate on the e-Delphi panel. Outcome measures: Consensus definition of severe asthma and criteria for assessing clinician behaviour index (CBI) and entering patients into the severe asthma registry. Results: After three e-Delphi rounds, experts achieved consensus on two definitions of severe asthma, 14 of 16 survey items for CBI and 10 of 11 items for registry entry criteria. 11 of 13 consensus criteria for CBI and 4 of 10 items for registry entry were voted as core data elements and are deemed feasible to be abstracted from electronic medical records (EMR). Conclusions: Expert consensus criteria have been developed for assessing clinician behaviour in asthma management and for entering patients into a primary care severe asthma registry.

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)0.155
metaresearch head score (Gemma)0.127
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.155
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1550.127
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0030.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0240.005

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 head0.412
GPT teacher head0.559
Teacher spread0.147 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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