Challenges in conducting long-term outcomes studies in critical care
Bibliographic record
Abstract
PURPOSE OF REVIEW: Evaluating longer term mortality, morbidity, and quality of life in survivors of critical illness is a research priority. This review details the challenges of long-term follow-up studies of critically ill patients and highlights recently proposed methodological solutions. RECENT FINDINGS: Barriers to long-term follow-up studies of critical care survivors include high rates of study attrition because of death or loss to follow-up, data missingness from experienced morbidity, and lack of standardized outcome as well as reporting of key covariates. A number of recent methods have been proposed to reduce study patients attrition, including minimum data set selection and visits to transitional care or home settings, yet these have significant downsides as well. Conducting long-term follow-up even in the absence of such models carries a high expense, as personnel are very costly, and patients/families require reimbursement for their time and inconvenience. SUMMARY: There is a reason why many research groups do not conduct long-term outcomes in critical care: it is very difficult. Challenges of long-term follow-up require careful consideration by study investigators to ensure our collective success in data integration and a better understanding of underlying mechanisms of mortality and morbidity seen in critical care survivorship.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this metaresearch. It is in the settled core of the field.
Review of the methodological challenges of long-term follow-up studies in critical care: attrition, missing data, lack of standardized outcomes and covariate reporting, and proposed methodological solutions; the object is how this research is done.
It directly examines methodological challenges in conducting long-term critical-care outcome studies.
Review of methodological challenges in conducting and completing long-term critical care outcomes research.
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 | 0.042 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".