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Record W2008160515 · doi:10.1111/ijs.12202

Quality in Acute Stroke Care (QASC): Process Evaluation of an Intervention to Improve the Management of Fever, Hyperglycemia, and Swallowing Dysfunction following Acute Stroke

2013· article· en· W2008160515 on OpenAlexaff
Peta Drury, Christopher Levi, Catherine D’Este, Patrick McElduff, Elizabeth McInnes, Jennifer Hardy, Simeon Dale, N. Wah Cheung, Jeremy Grimshaw, Clare Quinn, Jeanette Ward, Malcolm Evans, Dominique A. Cadilhac, Rhonda Griffiths, Sandy Middleton

Bibliographic record

VenueInternational Journal of Stroke · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Ottawa
FundersAustralian Diabetes Society
KeywordsMedicineSwallowingStroke (engine)Randomized controlled trialAcute strokeAuditMedical recordInternal medicineEmergency medicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Our randomized controlled trial of a multifaceted evidence-based intervention for improving the inpatient management of fever, hyperglycemia, and swallowing dysfunction in the first three-days following stroke improved outcomes at 90 days by 15%. We designed a quantitative process evaluation to further explain and illuminate this finding. METHODS: Blinded retrospective medical record audits were undertaken for patients from 19 stroke units prior to and following the implementation of three multidisciplinary evidence-based protocols (supported by team-building workshops, and site-based education and support) for the management of fever (temperature ≥37·5°C), hyperglycemia (glucose >11 mmol/l), and swallowing dysfunction in intervention stroke units. RESULTS: Data from 1804 patients (718 preintervention; 1086 postintervention) showed that significantly more patients admitted to hospitals allocated to the intervention group received care according to the fever (n = 186 of 603, 31% vs. n = 74 of 483, 15%, P < 0·001), hyperglycemia (n = 22 of 603, 3·7% vs. n = 3 of 483, 0·6%, P = 0·01), and swallowing dysfunction protocols (n = 241 of 603, 40% vs. n = 19 of 483, 4·0%, P ≤ 0·001). Significantly more patients in these intervention stroke units received four-hourly temperature monitoring (n = 222 of 603, 37% vs. n = 90 of 483, 19%, P < 0·001) and six-hourly glucose monitoring (194 of 603, 32% vs. 46 of 483, 9·5%, P < 0·001) within 72 hours of admission to a stroke unit, and a swallowing screen (242 of 522, 46% vs. 24 of 350, 6·8%, P ≤ 0·0001) within the first 24 hours of admission to hospital. There was no difference between the groups in the treatment of patients with fever with paracetamol (22 of 105, 21% vs. 38 of 131, 29%, P = 0·78) or their hyperglycemia with insulin (40 of 100, 40% vs. 17 of 57, 30%, P = 0·49). INTERPRETATION: Our intervention resulted in better protocol adherence in intervention stroke units, which explains our main trial findings of improved patient 90-day outcomes. Although monitoring practices significantly improved, there was no difference between the groups in the treatment of fever and hyperglycemia following acute stroke. A significant link between improved treatment practices and improved outcomes would have explained further the success of our intervention, and we are still unable to explain definitively the large improvements in death and dependency found in the main trial results. One potential explanation is that improved monitoring may have led to better overall surveillance of deteriorating patients and faster initiation of treatments not measured as part of the main trial.

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)0.002
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 score0.620
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.345
Teacher spread0.328 · 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

Citations43
Published2013
Admission routes1
Has abstractyes

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