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Record W4403627180 · doi:10.1016/j.ijnsa.2024.100248

From ‘strong recommendation’ to practice: A pre-test post-test study examining adherence to stroke guidelines for fever, hyperglycaemia, and swallowing (FeSS) management post-stroke

2024· article· en· W4403627180 on OpenAlexaff
Kelly Coughlan, Tara Purvis, Monique F. Kilkenny, Dominique A. Cadilhac, Oyebola Fasugba, Simeon Dale, Kelvin Hill, Megan Reyneke, Elizabeth McInnes, Benjamin McElduff, Jeremy Grimshaw, N. Wah Cheung, Christopher Levi, Catherine D’Este, Sandy Middleton

Bibliographic record

VenueInternational Journal of Nursing Studies Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsOttawa Hospital
FundersNational Health and Medical Research CouncilNational Heart Foundation of AustraliaMedical Research CouncilAustralian Government
KeywordsStroke (engine)SwallowingMedicineTest (biology)Physical therapyIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Background The Quality in Acute Stroke Care (QASC) Trial demonstrated that assistance to implement protocols to manage Fever, hyperglycaemia (Sugar) and Swallowing (FeSS) post-stroke reduced death and disability. In 2017, a ‘Strong Recommendation’ for use of FeSS Protocols was included in the Australian Clinical Guidelines for Stroke Management. We aimed to: i) compare adherence to FeSS Protocols pre- and post-guideline inclusion; ii) determine if adherence varied with prior participation in a treatment arm of a FeSS Intervention study, or receiving treatment in a stroke unit; and compare findings with our previous studies. Methods Pre-test post-test study using Australian acute stroke service audit data comparing 2015/2017 (pre-guideline) versus 2019/2021 (post-guideline) adherence. Primary outcome was adherence to all six FeSS indicators (composite), with mixed-effects logistic regression adjusting for age, sex, stroke type and severity (ability to walk on admission), stroke unit care, hospital prior participation in a FeSS Intervention study, and correlation of outcomes within hospital. Additional analysis examined interaction effects. Results Overall, 112 hospitals contributed data to ≥1 one Audit cycle for both periods (pre=7011, post=7195 cases); 42 hospitals had participated in any treatment arm of a FeSS Intervention study. Adherence to FeSS Protocols post-guideline increased (pre: composite measure 35% vs post: composite measure 40 %, aOR:1.2 95 %CI: 1.2, 1.3). Prior participation in a FeSS Intervention study (aOR:1.6, 95 %CI: 1.2, 2.0) and stroke unit care (aOR 2.3, 95 %CI: 2.0, 2.5) were independently associated with greater adherence to FeSS Protocols. There was no change in adherence over time based on prior participation in a FeSS Intervention study ( p = 0.93 interaction), or stroke unit care ( p = 0.07 interaction). Conclusions There is evidence of improved adherence to FeSS Protocols following a ‘strong recommendation’ for their use in the Australian stroke guidelines. Change in adherence was similar independent of hospital prior participation in a FeSS Intervention study, or stroke unit care. However, maintenance of higher pre-guideline adherence for hospitals prior participation in a FeSS Intervention study suggests that research participation can facilitate greater guideline adherence; and confirms superior care received in stroke units. Nevertheless, less than half of Australian patients are being cared for according to the FeSS Protocols, providing impetus for additional strategies to increase uptake.

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)0.012
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.467
Teacher spread0.383 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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