Calmer: a robot for managing acute pain effectively in preterm infants in the neonatal intensive care unit
Bibliographic record
Abstract
INTRODUCTION: For preterm infants in the neonatal intensive care unit, early exposure to repeated procedural pain is associated with negative effects on the brain. Skin-to-skin contact with parents has pain-mitigating properties, but parents may not always be available during procedures. Calmer, a robotic device that simulates key pain-reducing components of skin-to-skin contact, including heart beat sounds, breathing motion, and touch, was developed to augment clinical pain management. OBJECTIVE: Our objective was to evaluate the initial efficacy of Calmer for mitigating pain in preterm infants. We hypothesized that, compared to babies who received a human touch-based treatment, facilitated tucking, infants on Calmer would have lower behavioural and physiological pain indices during a single blood test required for clinical care. METHODS: Forty-nine preterm infants, born between 27 and 36 weeks of gestational age, were randomized either to facilitated tucking or Calmer treatment. Differences between groups in changes across 4 procedure phases (baseline 1, baseline 2, poke, and recovery) were evaluated using (1) the Behavioral Indicators of Infant Pain scored by blind coders from bedside videotape and (2) heart rate and heart rate variability continuously recorded from a single-lead surface ECG (lead II) (Biopac, Canada) sampled at 1000 Hz using a specially adapted portable computer system and processed using Mindware. RESULTS: No significant differences were found between groups on any outcome measures. CONCLUSION: Calmer provided similar treatment efficacy to a human touch-based treatment. More research is needed to determine effects of Calmer for stress reduction in preterm infants in the neonatal intensive care unit over longer periods.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".