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SOME CHEMICAL AND ELECTROCHEMICAL ASPECTS OF THE CHEMICAL MECHANICAL POLISHING OF COPPER

2002· article· en· W28751416 on OpenAlexfundaboutno aff
Ashraf T. Al‐Hinai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchReseau canadien de recherche respiratoireGenome Canada
KeywordsCopperPolishingElectrochemistryChemical-mechanical planarizationMetallurgyMaterials scienceChemistryElectrode

Abstract

fetched live from OpenAlex

A better understanding of the true burden of chronic obstructive pulmonary disease (COPD) needs to consider the implications of comorbidities. This study comprehensively examined the impact of comorbidities on excess direct medical costs in COPD patients.From health administrative data in British Columbia, Canada (1996-2012), we created a propensity-score-matched cohort of incident COPD patients and individuals without COPD. Health services use records were compiled into 16 major disease categories based on International Classification of Diseases codes. Excess costs (in 2015 Canadian dollars and converted to 2015 Euros; CAD1.000=EUR 0.706) were estimated as the adjusted difference in direct medical costs between the two groups.The sample included 128 424 subjects in each group. COPD patients generated excess costs of CAD5196/EUR3668 per person-year (95% CI CAD3540-8529), of which 26% was attributable to COPD itself and 51% was attributable to comorbidities (the remaining 23% could not be attributed to any specific condition). The major cost driver was excess hospitalisation costs. The largest components of comorbidity costs were circulatory diseases, other respiratory disorders, digestive disorders and psychological disorders (CAD696/EUR491, CAD312/EUR220, CAD274/EUR193 and CAD249/EUR176 per person-year, respectively).These findings suggest that the prevention and appropriate management of comorbidities in COPD patients may effectively reduce the overall burden of 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.486

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 designBench or experimental
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
Published2002
Admission routes2
Has abstractyes

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