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Toiminnanohjausjärjestelmän käyttöönotto - Osa muutosta Case Audiator-yhtiöt

2011· dissertation· en· W15459134 on OpenAlexaboutno aff
Erja Niittyviita

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Early intervention with budesonide is an effective strategy for mild persistent asthma, which has been shown to provide additional clinical benefits at a low incremental cost using USA cost data. The present authors analysed whether this strategy would be cost-effective using cost data for other countries. Based on the 3-yr prospective, randomised, double-blind inhaled Steroid Treatment As Regular Therapy (START) in early asthma study (comparing budesonide and placebo combined with usual asthma therapy), the cost-effectiveness was estimated separately for eight different countries, from both healthcare payer and societal perspectives, of adding budesonide to usual asthma therapy. Local unit costs were applied to data for the total trial population. Incremental cost-effectiveness ratios (ICER) were estimated as cost per symptom-free day (SFD) gained. Budesonide increased SFDs by an average of 14.1 days annually. From a healthcare payer perspective, budesonide would reduce the total cost of asthma care in Australia. In Sweden, Canada, France, Spain, UK, China and the USA, the ICER ranged from US$2.4-11.3 per SFD. From a societal perspective, budesonide would be cost-saving in Australia, Canada and Sweden. In conclusion, for countries where costs with budesonide are higher, the policy implication has to be determined by that health system's willingness to pay for an additional symptom-free day. However, where budesonide therapy increases symptom-free days and reduces total costs, the policy conclusion clearly favours early intervention.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.001

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 head0.084
GPT teacher head0.432
Teacher spread0.348 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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