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PCEC – Project Cost Estimating Capability

2014· article· en· W39389402 on OpenAlexaboutno aff
Bill Keeter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Individuals diagnosed with Chronic Obstructive Pulmonary Disease (COPD) are exposed to an increased risk of metabolic syndrome (MetS), which negatively affects their health outcomes and quality of life. Lifestyle interventions have shown promise in managing MetS. This study outlines the protocol for a web-based multimodal self-care program, Digital Metabolic Rehabilitation, for managing MetS in patients with COPD. The Digital Metabolic Rehabilitation is a single-arm pilot trial that integrates the Canadian Health Advanced by Nutrition and Graded Exercise (CHANGE) Program and a web-based wellness platform. The web-based wellness platform employed in this study is My Viva Plan (MVP)®, which integrates a holistic, multicomponent approach to promote wellness. The intervention will primarily focus on lifestyle changes for patients with COPD. Over 6 months, participants will use the web-based wellness platform and engage in weekly online support group sessions. Fifty patients diagnosed with stage I-II COPD and MetS will participate. Blood tests, anthropometrics, body composition, physical function, muscle strength, physical activity, energy metabolism, quality of life and mental health will be assessed at baseline, 3, and 6 months. The Digital Metabolic Rehabilitation program aims to explore whether a multimodal integrative intervention delivered through a web-based wellness platform can be implemented by patients with COPD with MetS. By combining the expertise of the CHANGE Program with the digital delivery format, the intervention seeks to enhance self-monitoring and foster better self-management practices. The protocol outlines a novel and potentially impactful intervention for managing MetS in patients with COPD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.239
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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