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Record W2611644011 · doi:10.1055/s-0038-1669601

Treatment and rehabilitation concepts for patients with addiction and concurrent disorders

2015· article· en· W2611644011 on OpenAlexaffabout
Manu Vogel, Mohammadali Nikoo, Michael Krausz

Bibliographic record

VenueDie Psychiatrie · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsRehabilitationAddictionHarmHarm reductionAbstinenceHealth careAddiction treatmentMedicineDetoxification (alternative medicine)PsychiatryNursingPsychologyPolitical scienceAlternative medicinePublic healthPhysical therapy

Abstract

fetched live from OpenAlex

Summary Background: Addiction and concurrent disorders give rise to a major burden of disease in both North America and Europe. However, these two continents have some fundamental differences in regards to the health care system and its funding as well as the types of vulnerable subpopulations to serve. For example, while emergency rooms are often the only available care for patients in the US and Canada due to financial barriers or structural deficits, stepped care approaches and separate rehabilitation systems are more commonplace in Europe. These differences can be observed not only on a transatlantic but also on an intra-European level. These differing attitudes and policies impact on treatment paradigms such as harm reduction, abstinence-based or opioid maintenance treatments etc. Structural components and clinical pathways lead to dissimilarities in access to care services such as detoxification, rehabilitation and community services. The role of primary care as an important treatment interface is much more recognized in Europe. While innovations are ongoing and scientific progress has been made in the treatment of concurrent disorders in recent years, implementing these findings into “real-world practice” has been insufficient so far.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.245

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 head0.047
GPT teacher head0.388
Teacher spread0.341 · 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 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

Citations1
Published2015
Admission routes2
Has abstractyes

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