The Unmet Need for Addressing Cardiac Issues in Intensive Care Research*
Bibliographic record
Abstract
OBJECTIVE: Patients with primary cardiovascular disorders and comorbidities are commonly admitted to ICUs; however, little is known about the current state of cardiac research being conducted in these adult ICU patients. DESIGN: Retrospective analysis. PATIENTS OR SUBJECTS: None. SETTING: In separate searches of ongoing phase II-IV clinical trials registered with ClinicalTrials.gov and funding grants available in the Canadian Institutes for Health Research funding decision database between 1999 and 2012, we identified all research initiatives focused on adult ICU patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome of interest was the proportion of cardiac-specific ICU studies, defined as any involving a cardiac population with a cardiac intervention (or observation for observational analyses) and/or a cardiac outcome. A total of 192 unique studies including adult ICU patients were identified from the ClinicalTrials.gov database. These were most commonly classified as respiratory or ventilation (19%), infectious (14.1%), or neurologic (12.0%) in focus. A total of 105 grants were identified in the Canadian Institutes for Health Research database. Funded studies most commonly addressed respiratory or ventilator questions (18.1%), infectious disease issues (12.4%), or hematological/thrombosis questions (9.5%). Only 4.6% of all ICU studies in ClinicalTrials.gov and 1.9% of all Canadian Institutes for Health Research grants could be considered cardiac. CONCLUSIONS: These findings highlight the relative paucity of cardiac-specific research in the intensive care setting relative to the high prevalence of acute cardiac diseases and comorbidities. This observed disparity warrants timely attention and should lead to meaningful research opportunities aimed at improving the outcomes of critically ill cardiac 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.230 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".