Blood Sampling in Newborns
Bibliographic record
Abstract
Objective of this study was to conduct a systematic review of YouTube videos showing neonatal blood sampling, and to evaluate pain management and comforting interventions used. Selected videos were consumer- or professional-produced videos showing human newborns undergoing heel lancing or venipuncture for blood sampling, videos showing the entire blood sampling procedure (from the first attempt or puncture to the time of application of a cotton ball or bandage), publication date prior to October 2014, Portuguese titles, available audio. Search terms included "neonate," "newborn," "neonatal screening," and "blood collection." Two reviewers independently screened the videos and extracted the following data. A total of 13 140 videos were retrieved, of which 1354 were further evaluated, and 68 were included. Videos were mostly consumer produced (97%). Heel lancing was performed in 62 (91%). Forty-nine infants (72%) were held by an adult during the procedure. Median pain score immediately after puncture was 4 (interquartile range [IQR] = 0-5), and median length of cry throughout the procedure was 61 seconds (IQR = 88). Breastfeeding (3%) and swaddling (1.5%) were rarely implemented. Posted YouTube videos in Portuguese of newborns undergoing blood collection demonstrate minimal use of pain treatment, and maximal distress during procedures. Knowledge translation strategies are needed to implement effective measures for neonatal pain relief and comfort.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".