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Record W2997794494 · doi:10.1016/j.cjco.2019.12.002

GOAL Canada: Physician Education and Support Can Improve Patient Management

2019· article· en· W2997794494 on OpenAlexafffundabout
Anatoly Langer, Mary Tan, Shaun G. Goodman, Jean‐Claude Grégoire, Peter Lin, G.B. John Mancini, James A. Stone, Cheryll Wills, Caroline Spindler, Lawrence A. Leiter

Bibliographic record

VenueCJC Open · 2019
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsUniversity of CalgarySt. Paul's HospitalUniversity of British ColumbiaCanadian Heart Research CentreMontreal Heart InstituteUniversité de MontréalSt. Michael's HospitalUniversity of Toronto
FundersJanssen PharmaceuticalsDuke Clinical Research InstituteJohnson and JohnsonBristol-Myers Squibb CanadaEsperion TherapeuticsFerring PharmaceuticalsNovo NordiskUniversity of TorontoLuitpold PharmaceuticalsDaiichi-SankyoMedicines CompanyRegeneron PharmaceuticalsSunovionHeart and Stroke Foundation of CanadaTenax TherapeuticsNew York UniversityActelion PharmaceuticalsYork UniversityPfizerNovartisKowa CompanySanofiMerckHLS TherapeuticsGlaxoSmithKlineBoehringer IngelheimAmgenServierBayerCSL BehringAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineMedical educationNursingFamily medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Background Despite the widespread use of statins, approximately 40% to 50% of Canadian patients with known cardiovascular disease do not achieve the low-density lipoprotein cholesterol (LDL-C) goal. G uidelines O riented A pproach to L ipid lowering (GOAL) is an investigator-initiated study aiming to ascertain the use of second- and third-line therapy and its impact on LDL-C goal achievement in a real-world setting. Methods GOAL enrolled patients with clinical vascular disease or familial hypercholesterolemia and LDL-C > 2.0 mmol/L despite maximally tolerated statin therapy. During follow-up, physicians managed patients as clinically indicated but with online reminders of guideline recommendations. Results Of 2009 patients enrolled (median age 63 years, 42% were female), baseline total cholesterol was 5.5 ± 1.4 mmol/L, LDL-C was 3.3 ± 1.3 mmol/L, non–high-density lipoprotein cholesterol was 4.1 ± 1.4 mmol/L, high-density lipoprotein cholesterol was 1.3 ± 0.4 mmol/L, and triglycerides were 2.0 ± 1.5 mmol/L. Lipid-lowering therapy used at baseline was statin therapy in 76% (with 24% statin intolerant) and ezetimibe in 25%. During follow-up, the proportion of patients achieving an LDL-C level of < 2.0 mmol/L increased significantly to 50.8% as a result of additional lipid-lowering therapy. Patients achieving the recommended LDL-C level were more likely to not be statin intolerant (83.8% vs 70.7%, P < 0.0001) and to be taking a high-efficacy type and dose of statin (52.4% vs 35.9%, P < 0.0001). The 3 top reasons for not using the recommended therapy with ezetimibe were patient refusal in 33%, not needed in 22%, and intolerance in 20%, whereas for PCSK9i the reasons were cost in 26%, not needed in 27%, or patient refusal in 25%. Conclusion The results indicate the feasibility of optimizing management, resulting in achievement of the guideline-recommended LDL-C level. This has the potential to translate into reductions in cardiovascular morbidity and mortality of Canadian patients.

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)0.006
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.274
Threshold uncertainty score0.551

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0330.003

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 head0.005
GPT teacher head0.241
Teacher spread0.236 · 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

Citations15
Published2019
Admission routes3
Has abstractyes

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