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Record W7161843778 · doi:10.82308/45864

The association between physician competence at licensure and the quality of asthma management and patient morbidity

2010· dissertation· en· W7161843778 on OpenAlexaboutno aff
Yuko Kawasumi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaMedical prescriptionMedical recordAsthma managementLogistic regressionHealth careCompetence (human resources)Receiver operating characteristicMEDLINE

Abstract

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Asthma imposes a substantial burden on patient health and health care expenditures. Persistent trends of sub-optimal asthma management and significant morbidity indicate the need to search for other key barriers and facilitators of quality of care. Through the use of administrative databases, this project first addressed the methodological challenge of identifying asthma patients, and then investigated the role of physicians and determinants of their approach to effective asthma management. Our objectives were 1) to develop an algorithm to identify patients with asthma, based on potential asthma-specific markers, from medical service and prescription claims databases, 2) to estimate the extent to which physician characteristics, specifically clinical competence, influenced the quality of asthma medication utilization and asthma morbidity. In the first study, 1,434 patients with confirmed asthma were identified from clinic medical records available through an existing electronic medical record project. Therapeutic indication for electronic prescriptions and the confirmed asthma from an inter-institutional automated problem list were used as the gold standard for physician-confirmed asthma. Using multiple logistic regression, we estimated the probability of the presence of asthma, using a combination of five groups of asthma-specific markers from administrative databases. Receiver Operating Characteristic (ROC) curves was used to assess the optimal cut-off probability of algorithms. The algorithm that showed the best performance in discriminating between the patients with asthma and those without it included indicators from medical services, pharmacy, and the demographic databases. The best fitting algorithm used a cut-off probability of 0.128 for asthma with sensitivity of 71%, specificity of 93%, and positive predictive value of 62%. In the second study, a prospective cohort of 609 physicians, who took the Medical Council of Canada (MCC) Part 2 examination between 1993 and1996 and provided a care for asthma patients in Quebec between 1993 and 2003 was assembled. Patients whose asthma was out-of-control at the index visit were followed for 6 months after the first visit with a study physician (index visit). Patients of physicians who achieved higher scores in communication (per 1 Standard Deviation (SD) increase in score) had a lower risk of persistent Fast-Acting Beta Agonist (FABA) overuse (OR=0.97; 95%CI: 0.94-1.0) and multiple ER visits for respiratory problems (OR=0.90; 95%CI:0.82-1.00). Higher MCCQE1, MCCQE2 and MCCQE2 communication scores were associated with a 4 to7% greater likelihood of inhaled corticosteroid (ICS) use (per 1SD increase). Similarly, higher scores achieved on the MCCQE1 as well as the MCCQE2 exams were associated with a 4 to 9% higher likelihood of the ICS/Total asthma medication (ICS plus FABA) ratio being >0.5 (per 1 SD increase). This project presents two major contributions. First, we demonstrated a unique and practical methodological approach to identify patients with asthma from administrative claims databases for the future assessment of asthma management. Second, we identified important physician abilities for effective management of patients with out-of-control asthma.

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)0.008
metaresearch head score (Gemma)0.065
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 score0.011
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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