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Record W2411837195

Application of the 1998 Canadian cholesterol guidelines to a military population: health benefits and cost effectiveness of improved cholesterol management.

2003· article· en· W2411837195 on OpenAlexaffabout
Johanna N. Spaans, Doug Coyle, George Fodor, Rama C. Nair, Régis Vaillancourt, Steven A. Grover, Louis Coupal

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineLife expectancyInterimStatinPopulationCost effectivenessCoronary artery diseaseCholesterolEnvironmental healthInternal medicineRisk analysis (engineering)
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether statins are underprescribed in the Canadian military. The cost effectiveness of statin therapy in patients identified by the 1998 Canadian cholesterol interim guidelines was also explored. METHODS: Charts of 1424 Canadian military personnel (age 45 or older) were reviewed at 11 Canadian bases. Risk factors and cholesterol values were used to identify drug therapy candidates. Cost effectiveness ratios and health benefits in terms of years of life saved for statin therapy were estimated for the candidates using a validated cardiovascular disease life expectancy model. RESULTS: Of the 1313 personnel not on lipid lowering medication, 172 were identified as drug therapy candidates. An average of 2.89 years of life saved was forecast for the identified personnel, at an average cost of less than 10,000 dollars per year of life saved. CONCLUSIONS: The health benefits of statin therapy in this population are substantial and the cost effectiveness is acceptable. Statin therapy warrants greater attention as a preventive strategy for coronary artery disease.

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)0.001
metaresearch head score (Gemma)0.010
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 score0.076
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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

Citations6
Published2003
Admission routes2
Has abstractyes

Explore more

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