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Record W3206669213 · doi:10.1161/strokeaha.121.034124

Frequency and Prognostic Significance of Clinical Fluctuations Before Hospital Arrival in Stroke

2021· article· en· W3206669213 on OpenAlexaff
José G. Romano, Hannah Gardener, Eric E. Smith, Iszet Campo‐Bustillo, Yosef Khan, Sofie Tai, Nikesha Riley, Ralph L. Sacco, Pooja Khatri, Heather M. Alger, Brian Mac Grory, Deepak Gulati, Navdeep Sangha, Karin Olds, Curtis Benesch, Adam G. Kelly, Scott S Brehaut, Amit Kansara, Lee H. Schwamm, Scott Moody, Weiping Ye, Vena Sobhawongse, Jeffrey M. Craig, Heloisa Pearson, Deborah Summers, Christine Boerman, Christy Rice, Robin Kintner, Mayumi Oka, Sarah E. Baran, Christina Roels, Maureen Dosunmu, Cherylee W. J. Chang, Jennifer L. Moran, Denise Ditrich, Nicholas Lanciano, Aimee Mann, C. Romero, David Salvatore, Annette Kujawski Taylor, Neel Shah, Rodney Leacock, A. Rochester, Fanny Guillerminet, Jerry C. Martin, Johnny Jones, Nicol Brandon, Vikas Grover, Maryika Gibson, Maheen Malik, C. Crawford Mechem, W. R. Logan, Camilla Cook, Muhib Khan, Christa Rood, Arun Babu, Leah Steinig, Jestin N. Carlson, Mélanie Henderson, Gabriel Vidal, Bethany Jennings, Jennifer Lynch, Jessica Ratcliff, Kathryn Kirchoff, Khadean Moncrieffe, Jennifer Rasmussen-Winkler, Leigh Allen, G.E. Thompson, Christopher Firek, Stephen Martino, Baher Georgy, Gillian Gordon Perue, Nina Vekima, Kasey Gildersleeve, Marian Skewes, Christina Valdovinos, Timothy C. Parsons, Cynthia Marques, John W. Chen, David Lombardi, Brenda Perez, Amer Malik, Kathy Hesse, Amy Guzik, Sandra E. Norona, Robert Hoesch, Jacki Anderson, Dorothea Altschul, Farah Fermin, Miran Salgado, Jonathan Muller, Indrani Acosta, Brooke Hartwell, Terry A. Neill, Carrie Hundley, Abhineet Chowdhary, Tina Fortney, José R. Romero, Brandon Finn, Refat Assad, Maggie Ellithorpe, Rebecca Sugg, Susan Hetzel, Muhammad M. Alvi, Jay Sherman, Jonathan Hartman, Tashia Orr, Ankur Garg, Melissa Turner, Curtis A. Given, Sara Renfrow, Jeffrey Hilburn, Ellen Looney, Christopher Commichau, Paul Jarvis, Changsoo Hahm, Melissa Mccaulley, Angel Pulido, Sergio Michel, Nima Ramezan‐Arab, Françoise Toussaint- Jones, Anna Khanna, Esther Olasoji, Armistead Williams, Elizabeth Purrington, R. Nagaraja Reddy, Renee Potter, Bhupat Desai, Karen Tse-Chang, Laurence Ufford, Leslie Drager, Keith O. Jones, Teresa Ellebusch, Michelle Dobrzynski, Elizabeth H. Wise, Ann Jerde, Gauhar Chaudhary, Robyn McLean, Joseph Hanna, Dana P. Cook, Franklin A. Marden, Jennifer Orde, Ajay Arora, Shawna Miller, Raymond Reichwein, Deborah Hoffman, Kelly Matmati, Nabil Matmati, Kumiko Owada, Laura Murphy, Ashish Masih, Bethany Fife, Larry Shepherd, Stephen T. Gancher, Sabrina Enoch, Matthew Smith, Denise Goings, Joseph Mazzola, Edward Plyler, Lisa Landers, James Napier, Laura Thoreson, Amer Alshekhlee, Michelle Raymond, Tarakad S. Ramachandran, Michael R. Jorolemon, David J Padalino, Collin Maloney, J Mott, Laxmi Dhakal, Cindy Murphy, Truman J. Milling, Patrick Lawrence, Harish Shownkeen, Kathy Hansen, Paul A. Cullis, Lynne Froehlich, Sajjad Mueed, Ryan Pavolka, Steven R. Levine, Nadege Gilles, Laura LaChance, Karen Klein, Rose Dotson, Kristopher Rowe, Elisheva Coleman, Emily Sayles, Rajan Gadhia, Jason Lee, Paul W. Lewis, Jenny Nunley, Rehan Sajjad, C. Halliday, Angelos Katramados, Theresa Holmes, Rashmikant Kothari, Linda C. Mader, Fen Lei Chang, Kelly Western, Kinjal Desai, Colleen Kehr, Gary Reese, Ashu Jadhav, M Steinbach, Jeffrey L. Saver, Gilda Avila, Janice A. Miller, Alicia Gneiting, Matthew Tenser, Sarah E. Burke

Bibliographic record

VenueStroke · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineStroke (engine)Modified Rankin ScaleObservational studyBarthel indexEmergency medicineArrival timeHospital dischargeIschemic strokePhysical therapyInternal medicineActivities of daily livingIschemia

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Clinical fluctuations in ischemic stroke symptoms are common, but fluctuations before hospital arrival have not been previously characterized. METHODS: A standardized qualitative assessment of fluctuations before hospital arrival was obtained in an observational study that enrolled patients with mild ischemic stroke symptoms (National Institutes of Health Stroke Scale [NIHSS] score of 0-5) present on arrival to hospital within 4.5 hours of onset, in a subset of 100 hospitals participating in the Get With The Guidelines-Stroke quality improvement program. The number of fluctuations, direction, and the overall improvement or worsening was recorded based on reports from the patient, family, or paramedics. Baseline NIHSS on arrival and at 72 hours (or discharge if before) and final diagnosis and stroke subtype were collected. Outcomes at 90 days included the modified Rankin Scale, Barthel Index, Stroke Impact Scale 16, and European Quality of Life. Prehospital fluctuations were examined in relation to hospital NIHSS change (admission to 72 hours or discharge) and 90-day outcomes. RESULTS: Among 1588 participants, prehospital fluctuations, consisting of improvement, worsening, or both were observed in 35.5%: 25.1% improved once, 5.3% worsened once, and 5.1% had more than 1 fluctuation. Those who improved were less likely and those who worsened were more likely to receive alteplase. Those who improved before hospital arrival had lower change in the hospital NIHSS than those who did not fluctuate. Better adjusted 90-day outcomes were noted in those with prehospital improvement compared to those without any fluctuations. CONCLUSIONS: Fluctuations in neurological symptoms and signs are common in the prehospital setting. Prehospital improvement was associated with better 90-day outcomes, controlling for admission NIHSS and alteplase treatment. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02072681.

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)0.001
metaresearch head score (Gemma)0.011
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.310
Teacher spread0.291 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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