PEARL – Pain in early life. A new network for research and education
Bibliographic record
Abstract
Abstract Aims To establish a network for research and education and to provide expert knowledge to parents and health care professionals about pain in early life. Methods In November 2014 a group of Nordic researchers and research students, committed to the field of pain in early life, gathered for an open lecture day and workshop in Örebro, Sweden. Inspired by the work of the Canadian initiative PICH – Pain In Child Health, the network formulated it’s vision: To be a stabile and competent research and training network within the area of pain in early life. A first collaborate project was designed: “Translation, cultural adaptation and validation of the revised version of the Premature Infant Pain Profile (PIPP-R): An effort to improve pain assessment in infants in the Nordic countries”. Results Fourteen months later, in January 2016, the second PEARL-meeting was held, in Oslo, Norway. The lecture day provided clinically active nurses and physicians from several countries with the latest findings on how to best manage pain in neonatal settings. The network which now consist of 18 researchers from different professions and academic levels presents itself on a five-language website: www.pearl.direct . The PIPP-R project has progressed according to the plan. The PIPP-R is translated into Finnish, Icelandic, Norwegian and Swedish. The cultural adaptation and validation should be finished in fall 2016. The members work on and plan for further collaborate projects. The next two steps are to translate and distribute educational material for parents via Internet and social media, and to establish a research and mas-ters course about pain in early life. The work has been secured by funding from Örebro University and Örebro University Hospital Research Foundation. Conclusions PEARL fulfils the need for a collaborative network for pain in early life researchers in the Nordic countries.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.090 | 0.033 |
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".