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.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".