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Record W4406993503 · doi:10.1161/str.56.suppl_1.wmp67

Abstract WMP67: Circadian Variation In Collateral Status: The HERMES Collaboration

2025· article· en· W4406993503 on OpenAlexaff
Muhammad Bilal Tariq, Scott A. Brown, D Liebeskind, Steffen Tiedt, Ronda Lun, Gregory W. Albers, Katherine T Mun, Charles B.L.M. Majoie, Andrew M. Demchuk, Tudor Jovin, Peter Mitchell, Bruce Campbell, Serge Bracard, Françis Guillemin, Keith W. Muir, Phil White, Diederik W.J. Dippel, Michael Hill, Mayank Goyal, Jeffrey L. Saver

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineCircadian rhythmCollateralVariation (astronomy)Collateral damageInternal medicineCardiology

Abstract

fetched live from OpenAlex

Background: Prior studies have demonstrated that the incidence of stroke and rate of infarct progression varies across the 24-hour cycle and that circadian rhythm is associated with the brain’s response to reperfusion and neuroprotection. However, the pathophysiology of this variation is poorly understood. In this study we assessed changes in collateral status with time of day as a possible mechanism behind this variation. Methods: Using data from the 7 randomized EVT trials in the HERMES collaboration, patients with analyzable baseline CT angiograms were selected. Presenting collateral status on CTAs was rated using the Regional Collateral Score (rCS). The effect of time of onset (last known well) and time of scan on collateral status was assessed. Time of day was analyzed as: 1) binary day-night (23:00–06:59/07:00–22:59); 2) 4-hour intervals (23:00–02:59/03:00–06:59/07:00–10:59/11:00–14:59/15:00–18:59/19:00–22:59) and 3) a continuous variable as per Consortium International pour la Recherche Circadienne sur l’AVC recommendations. Results: Among 1250 patients with LVOs, 1086 (86.9%) had daytime and 164 (13.1%) had night-time stroke onset. Baseline demographic and clinical features of patients that differed (Table 1) were night-time onset patients were younger (62.0 vs 67.1 years), less often had highly severe (NIHSS>16) deficits (43.6% vs 56.3%), had longer onset-to-arrival (186 vs 122 minutes), and longer onset-to-imaging (218 vs 159 minutes). In multivariable analysis, categorizing based on last known well time, there were no significant difference in rCS score by night vs day (7.5±2.0 vs 7.4±2.3 p=0.99) or by 4-hour intervals (7.8±1.9 vs 7.3±2.1 vs 7.4±2.2 vs 7.5±2.2 vs 7.5±2.3 vs 7.1±2.4 p=0.24) (Figure 1). Similarly, categorizing by scan time, there were no significant differences in rCS score by night vs day (7.3±2.3 vs 7.4±2.2 p=0.60) or by 4-hour intervals (7.2±2.4 vs 7.6±2.0 vs 7.3±2.2 vs 7.4±2.1 vs 7.5±2.3 vs 7.6±2.3 p=0.22) (Figure 1). The relationship between continuous time and rCS mapped by LOESS regression also did not indicate substantial rCS variation with time of onset (Figure 2). Conclusion: Our study does not demonstrate a strong relationship between time of day and regional collateral score. This was at least partially driven by a ceiling effect as most patients had good collateral scores. Further studies to identify patient and imaging characteristics that may explain the relationship between circadian variation of stroke outcomes are needed.

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)0.010
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 score0.010
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0040.000

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 head0.019
GPT teacher head0.299
Teacher spread0.280 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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