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Record W2068228021 · doi:10.1080/02699050701202027

Management of heterotopic ossification and venous thromboembolism following acquired brain injury

2007· review· en· W2068228021 on OpenAlexafffund
Nora Cullen, Mark Bayley, Nestor A. Bayona, Maureen Hilditch, Jo-Anne Aubut

Bibliographic record

VenueBrain Injury · 2007
Typereview
Languageen
FieldMedicine
TopicHeterotopic Ossification and Related Conditions
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteUniversity of TorontoToronto Rehabilitation Institute
FundersOntario Neurotrauma Foundation
KeywordsHeterotopic ossificationMedicineVenous thromboembolismOssificationSurgeryThrombosis

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the effectiveness of interventional strategies for the common complications of heterotopic ossification (HO) and venous thromboembolism (VTE) following acquired brain injury (ABI). METHODS AND MAIN OUTCOMES: A systematic review of the literature from 1980-2005 was conducted focusing on interventions for HO and VTE in the ABI population. Nineteen studies examining a variety of treatment approaches were evaluated. RESULTS: The majority of interventions are supported by limited evidence, defined as an absence of randomized controlled trials (RCTs). All of the treatment approaches for HO are supported with limited evidence. For VTE, there is moderate evidence, defined as at least one positive RCT, indicating that low-molecular-weight heparin is more effective than low-dose unfractionated heparin in preventing VTE, low-molecular-weight heparin is as effective and safe as unfractionated heparin for the prevention of pulmonary thromboembolism, low-molecular-weight heparin combined with compression stockings is more effective than compression stockings alone for the prevention of VTE and intermittent pneumatic compression devices are as effective as low-molecular-weight heparin for the prevention of VTE. CONCLUSIONS: There are a variety of intervention and prophylactic strategies that have been postulated to treat and reduce the incidence of these complications, with the goal of improving rehabilitation outcomes. It is therefore important to investigate the efficacy of these treatment strategies to provide guidance for clinical practice based on the best available evidence.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.063
GPT teacher head0.393
Teacher spread0.330 · 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.

Study designNot applicable
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

Citations19
Published2007
Admission routes2
Has abstractyes

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