MétaCan
Menu
← Back to cohort
Record W1557876356 · doi:10.1161/str.46.suppl_1.tmp102

Abstract T MP102: Hotline Use for Recruitment Support in Acute Stroke Trials: Lessons learned To Date in Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH)-II

2015· article· en· W1557876356 on OpenAlexaff
Kathryn A France, Mushtaq Qureshi, Jessy Thomas, Emily Abbott, Logan Brau, Adnan I. Qureshi

Bibliographic record

VenueStroke · 2015
Typearticle
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsAbbott (Canada)
Fundersnot available
KeywordsHotlineMedicineClinical trialContext (archaeology)Stroke (engine)Acute strokeRandomizationMedical emergencyEmergency medicineRandomized controlled trialEmergency departmentInternal medicineNursingTelecommunications

Abstract

fetched live from OpenAlex

Background: Use of hotline services for clinical support and safe operation of a research trial is common and important. The value of such services has not been objectively assessed within the context of a large acute stroke clinical trial. Methods: The use of three different hotline services have been tested, and cell and Email- options have also been explored for their advantages. US and Non-US sites are provided access to central hotline services, but for efficiency a local network for managing calls is established in each region. All sites may access both data management and the trial PI via hotline services when needed and this is supported by other technologies in addition. A means for supporting overall trial communications in light of these interactions has been developed and valuable insights are gained. Results: Data gathered from 211 calls logged at the Clinical Coordinating Center through the course of the ATACH-II trial have been summarized: Total Calls Reviewed: 211; Year 2 of trial: 92 calls, Year 3 of trial: 100 calls. Calls received midnight to 8 AM: 19 (9.0%), 8 AM - 5 PM 130 (61.6 %) 5 PM - 12:00 PM 65 (30.8%). Issues Resolved in < 5 min 143/211 (67.8%) Taking > 30 min to resolution 51/211 (24.2%). Purpose of calls: Eligibility 89 (42.2%). protocol compliance including drug management: 77 (36.5%%), randomization/emergency randomization: 16 (7.6%), protocol deviation:13 (6.2%), technological difficulties: 12 (5.7%), AE/SAE: 9 (4.3%), Subject enrollments directly associated with calls: 57 (20.8% of domestic subjects); excluded candidates directly associated with calls: 46 (% not available). Conclusions: In an international trial requiring rapid enrollment of subjects with intracerebral hemorrhage, the role of direct support via a hotline and other immediate communications means has proven to be instrumental in maintaining good protocol compliance and supporting enrollment by site team members .

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)0.051
metaresearch head score (Gemma)0.135
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.135
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0290.008

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 head0.643
GPT teacher head0.533
Teacher spread0.110 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueStroke→Same topicArtificial Intelligence in Healthcare and Education→French-language works237,207→