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Record W2941275507 · doi:10.1097/jnn.0000000000000385

Effectiveness of Initiating Deep Vein Thrombosis Prophylaxis in Patients With Stroke: An Integrative Review

2018· review· en· W2941275507 on OpenAlexaboutno aff
Mark Angel M. Dizon, Josephine M. De Leon

Bibliographic record

VenueJournal of Neuroscience Nursing · 2018
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDeep veinChemoprophylaxisStroke (engine)ThrombosisVenous thrombosisCompression stockingsIntermittent pneumatic compressionIncidence (geometry)Intensive care medicineComplicationThromboembolic strokeSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Venous thromboembolism (VTE) is a frequent and potentially fatal complication of immobility caused by cerebrovascular disease. This review examines the efficiency of deep vein thrombosis (DVT) prophylaxis methods. Patients with stroke initiated on DVT prophylaxis were compared with those who did not have any prophylaxis. Integrative review research design was used and included articles from 2010 to 2016. Search terms such as "DVT prophylaxis" and "stroke" were used to identify scientific publications. Of 173 studies identified, 12 articles were included and rated using the Canadian Medical Association and Center for Evidence-Based Medicine Level of Evidence ranking system. Of DVT prophylaxis methods identified, intermittent pneumatic compression device was superior to antiembolic stockings. Current data showed that the stockings were insufficient in preventing VTE. Heparin and low-molecular-weight heparin were efficient chemoprophylaxis in reducing the incidence of VTE. The combination of chemical and mechanical DVT prevention is recommended.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.376
Teacher spread0.336 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations17
Published2018
Admission routes1
Has abstractyes

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