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Record W3042146863 · doi:10.1161/strokeaha.120.029734

Provincial Door-to-Needle Improvement Initiative Results in Improved Patient Outcomes Across an Entire Population

2020· article· en· W3042146863 on OpenAlexaffabout
Noreen Kamal, Thomas Jeerakathil, Jillian Stang, Mingfu Liu, Edwin Rogers, Eric E. Smith, Andrew M. Demchuk, Muzaffar Siddiqui, Balraj Mann, Jennifer Bestard, Eddy Lang, Elaine Shand, Magali Benard, Lisa Collins, Kevin Martin, Corinna Hartley, Marnie Reiber, Shelley Valaire, Kelly Mrklas, Michael D. Hill, Dana Allen, Bill Anderson, Joanne Angelstand, Emmanuel Anokye, Cherylyn Antymniuk, Natalie Arsenault, Brenda Ashman, Kathy Baker, Jackie Bakker, Darla Balenga, Melissa Berg, Lori-Ann Berry, Michael Betzner, Lauren Page Black, Denise Blain, Tracy Boutilier, Joanne E. Brady, Stacey Lynn Brewster, Patricia E. Brown, Ken Buchynski, Erin Bugbee, Michael J. Bullard, D. J. Burke, Chloe Burnett, Kenneth Butcher, Paula Cackett, Hal Canham, Ann Chiovetti, Lana Chivers, Caitlin Cobb, M.C. Cote, Shelagh B. Coutts, Deb Currie, J Daniels, Natasha Desouza, Mona Diebert, Tanya Dixon, Jennifer Dotchin, Sandy Duckett, Verlyne Dustow, Robyn Dwyer, Margaret Dymond, Cathy Edmond, Muneer Eesa, Nicole M. Elias, Tara Elliott, Simone Empson, L. A. Falls, Mark Forder, R. Foreman, Darrel Forsythe, Twyla Fortier, Laura Fowler, S L Franklin, Jim Garland, Catherine Garon, Darrin Gerl, Imran Ghauri, S. Gough, Kevin Govender Mark, Gripp Mary-Lou Halabi, Sandy Halldorson, Jean Harsch, Caroline Hatcher, Kathryn Hebner, Richard Hemsley, Denise Holman, Scott Holsworth, S. Stanley Holton, G Hull, Barb Hyciek, Richard Ibach, Oje Imoukhuede, Barbara Jeal, Denman Jill, M. Pontier Johnson, Ozi Jones, Alison Kabaroff, Hayrapet Kalashyan, Frddy Kay, Pam Kaytor, Tim Keppy, P King, Sharon Kiszszak, Ryan Klick, Evelyn Koshurba, Rob Kruhlak, Julie Lacasse, Mike Lane, Tamara Laughs, Leslie Laut-Barss, Pam Lavalee, Tania Leclair, P. F. Linden, Troy Linderman, J.I. Livingstone, Marilyn Lodder, Karen Lundgard, E.A. Lyle, Kathleen Mackenzie, Ashleigh Malarczuk, Dave Malfair, Herbert Manosalva Alzate, Kerry McCann, Shannon McCarthy, Melissa D. McKenzie, Leta McRobert, Dana Meroniuk, Ryan Millar, Rob Miller, Bilan Mir, John Montpetit, Jackie Morissette, Laurel Morrison, Fiona Murray-Galbraith, Firdaus Mydeen, Lakshmi Namagiri, Nicole Neidig, G.R. Neil, Nancy Newcommon, Craig Newell, Chris Nichol, Colleen M. Norris, D Norton, Sara Noseworthy, Linda O’Hara, Shannon O’Neail, Wes Orr, Elysia Panes, Tita Panes, Joyce Paradis, Teresa Parry, D. P. S. Peacock, Thomas C. Peebles, Steen V. Petersen, Ian G. Phelps, Roy Pooley, Nicholas Potvin, Rosanne Pryor, Max Ramsahoye, Mohammad Rashead, Kevin Reedyk, Darla Reynolds, Steven L. Rideout, Kara Rimmer, Emad Salih, Patrick San Agustin, Darren Sandbeck, Salim Sattar, Natalie Sauter, Kim E. Schmidt, Elizabeth Seib, Jason Selzler, Bill Sevcik, Donna Sharman, Ashfaq Shuaib, D. E. Smith, Brent Snider, Jonathan L. Snider, J Stander, Caroline J. Stephenson, Candice Stewart, Carol Stoyberg, Yvonne Suranyi, Myles Tablin, Colleen Taralson, Janice Throndson, Kristen Traverse, Dalique van der Nest, Tyler Van Mulligan, Cornelius Van Vuuren, Erich Vanderlinde, Renee Vilneff, Glenda Volk, Krystal Wall, De Juan Wang, Dave Warharft, J G R Watson, Lorraine Weir, Dale Weiss, Dave Welch, Toni Winder, Winona Winsor, Douglas Woudstra, Dana Youn, Leane Young, Charlotte Zerna

Bibliographic record

VenueStroke · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsLloydminster HospitalChinook Regional HospitalAlberta Health ServicesRed Deer Regional HospitalGrey Nuns Community HospitalWest Park Healthcare CentreGovernment of SaskatchewanAlberta HealthUniversity of AlbertaUniversity of CalgaryDalhousie University
Fundersnot available
KeywordsMedicinePopulationStroke (engine)Emergency medicineEnvironmental health

Abstract

fetched live from OpenAlex

Background and Purpose: Improving door-to-needle times (DNTs) for thrombolysis of acute ischemic stroke patients improves outcomes, but participation in DNT improvement initiatives has been mostly limited to larger, academic medical centers with an existing interest in stroke quality improvement. It is not known whether quality improvement initiatives can improve DNT at a population level, including smaller community hospitals. This study aims to determine the effect of a provincial improvement collaborative intervention on improvement of DNT and patient outcomes. Methods: A pre post cohort study was conducted over 10 years in the Canadian province of Alberta with 17 designated stroke centers. All ischemic stroke patients who received thrombolysis in the Canadian province of Alberta were included in the study. The quality improvement intervention was an improvement collaborative that involved creation of interdisciplinary teams from each stroke center, participation in 3 workshops and closing celebration, site visits, webinars, and data audit and feedback. Results: Two thousand four hundred eighty-eight ischemic stroke patients received thrombolysis in the pre- and postintervention periods (630 in the post period). The mean age was 71 years (SD, 14.6 years), and 46% were women. DNTs were reduced from a median of 70.0 minutes (interquartile range, 51–93) to 39.0 minutes (interquartile range, 27–58) for patients treated per guideline ( P <0.0001). The percentage of patients discharged home from acute care increased from 45.6% to 59.5% ( P <0.0001); the median 90-day home time increased from 43.3 days (interquartile range, 27.3–55.8) to 53.6 days (interquartile range, 36.8–64.6) ( P =0.0015); and the in-hospital mortality decreased from 14.5% to 10.5% ( P =0.0990). Conclusions: The improvement collaborative was likely the key contributing factor in reducing DNTs and improving outcomes for ischemic stroke patients across Alberta.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.709
Threshold uncertainty score0.585

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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

Citations33
Published2020
Admission routes2
Has abstractyes

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