Systematic review and meta‐analysis suggest that varying prevalence of non‐acute pain in critically ill infants may be due to different definitions
Bibliographic record
Abstract
AIM: Our aim was to quantify the prevalence of non-acute pain in critically ill infants and to identify how non-acute pain was described, defined and assessed. METHODS: This systematic review and meta-analysis used multiple electronic databases to search for papers published in any language to March 2018: 2029 papers were identified, and 68 full texts were screened. Studies reporting the prevalence of non-acute pain in infants younger than 2 years and admitted to critical care units were included. The extracted data included the use of non-acute pain descriptions, definitions and pain assessment tools. RESULTS: We included 11 studies published between 2002 and 2018 that comprised 1204 infants from Europe, the USA, Canada and India. They were prospective observational (n = 7) and retrospective observational (n = 1) studies and randomised controlled trials (n = 3). The prevalence of non-acute pain was 0%-76% (median 11%). Various pain assessment tools were used, and only two could be pooled. This gave a pooled prevalence of 3.7%-39.8%. A number of different descriptors were used for non-acute pain, and all of these were poorly defined. CONCLUSION: The prevalence of non-acute pain in infants admitted to critical care units varied considerably. This could have been because all the studies used different definitions of non-acute pain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".