Nicotine Replacement Therapy in the Intensive Care Unit
Bibliographic record
Abstract
OBJECTIVE: The objective of this review was to systematically review and evaluate available literature describing the effect of nicotine replacement therapy (NRT) on mortality and other outcomes in nicotine-dependent critically ill patients admitted to the intensive care unit (ICU). DATA SOURCES: A systematic search of the following databases was performed: MEDLINE (1948-August 2011), EMBASE (1980-August 2011), Cochrane Database of Systematic Reviews, International Pharmaceutical Abstracts (1970-August 2011), Google, and Google Scholar. STUDY SELECTION: Studies that reported outcomes associated with any form of NRT in any intensive care setting were included. Studies were included regardless of design or number of participants reported. Studies published in languages other than English were excluded. DATA EXTRACTION: Data from each study were extracted using a standardized data extraction tool. Information included the study design, number of patients, classification of ICU, baseline characteristics, outcomes assessed, and overall results. DATA SYNTHESIS: Our search identified 8 studies, of which 7 met the inclusion criteria. These 7 studies were qualitatively reviewed and critically appraised for methodological quality, robustness of results, and internal and external validity. The results of similar studies and populations were compared in order to draw conclusions pertaining to specific intensive care settings. CONCLUSIONS: We conclude that NRT should not be routinely prescribed to patients admitted to intensive care settings. With only equivocal evidence of efficacy and signals suggesting increased toxicity, we believe that its use should be limited to selected patients where the potential benefit clearly outweighs the risk. There is a need for adequately powered randomized controlled trials to confirm the benefits and risks of NRT in the ICU overall but also in its unique subpopulations.
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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".