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Record W2104696087 · doi:10.1186/cc3330

ICU research coordinator activities: a time-in-motion multicenter study

2005· article· en· W2104696087 on OpenAlexaffabout
Ellen McDonald, France Clarke, Craig Dale, Carson Davidson, Rebecca Trantowski Farrell, Lori Hand, Tracy McArdle, Orla Smith, Marilyn Steinberg, Irene Watpool, Richard W. Ward, Diane Heels‐Ansdell

Bibliographic record

VenueCritical Care · 2005
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of OttawaUniversity of TorontoMcMaster University
FundersNational Institutes of Health
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

ICU Research Coordinators (ICURCs) represent a growing membership in the Canadian Critical Care Trials Group (CCCTG). The professional contribution of ICURCs to clinical critical care research is profound, but has not been well characterized. To describe the settings, studies, resources, tools, and activities of Canadian ICURCs. We conducted a prospective observational time-in-motion study of CCCTG ICURCs from 20 September to 15 October 2004. We generated items and domains in focus groups. The instrument was formatted using both open and closed ended responses. We piloted for comprehensiveness and clarity. We obtained data on the setting, studies, tools and resources available in each center, then used a weekly management log to document activities under seven domains. Completed forms were faxed to the Methods Center at St Joseph's Healthcare and entered into an Excel Spreadsheet for descriptive analyses. Twenty ICURCs participated from 13 mixed ICUs (100% university-affiliated hospitals with 440 ± 225 hospital beds and 23 ± 11 ICU beds). Over 4 weeks in 13 ICUs, 104 ± 64 patients were admitted, 74 ± 60 were screened and 9 ± 9 were enrolled in studies. On average, there were 4.6 ± 2.1 peer reviewed, and 3.8 ± 3.5 industry studies; 5.8 ± 3.4 were RCTs, 2.8 ± 2.3 were observational and 0.8 ± 1.2 were other designs. On average, 7.1 ± 5.8 studies enrolled adults and 1.5 ± 2.4 enrolled children. These resources were accessed electronically: laboratory data (100%), diagnostic images (53.8%), radiology reports (69.2%), ECGs (15.4%) and clinical information systems (15.4%). Data collection tools include: paper (100%), NCR paper (38.5%), datafax (38.5%), laptop (7.7%), PDAs (0%), and Web-based approaches (84.6%). Time spent in each of seven activities was: study preparation (literature review, inhouse protocol development, REB submissions and amendments, budget preparation, CRF development and piloting, regulatory document assembly, in-services), 16.5%; research conduct (screening, consent and family meetings, pharmacy and other study supplies, data collection and queries, conducting and preparing tests, AE and SAE reporting, bedside protocol compliance), 44.1%; communication (email/ telephone calls/mail, staff education), 14.3%; management (team meetings, multicentered management, screening and finance logs, initiation audit and close-out visits), 13.5%; data management (database creation and data entry, analysis), 3.9%; dissemination (interim and final reports, conference preparation and presentation), 3.0%; and miscellaneous, 4.7%. CCCTG ICURCs work primarily in mixed university-affiliated ICUs, conducting randomized trials in adults. The availability of electronic resources is modest, and variable data collection methods are employed. ICURCs engage in many activities to prepare, implement, and manage investigations for critically ill patients.

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)0.025
metaresearch head score (Gemma)0.035
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 score0.063
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.211
GPT teacher head0.553
Teacher spread0.343 · 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

Citations3
Published2005
Admission routes2
Has abstractyes

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