Bibliographic record
Abstract
Neonates admitted to neonatal intensive care units are exposed to a high number of painful procedures for their survival. Faced with a pain that is predictable, it is imperative to implement analgesia before carrying out the procedure, to reduce the impact of the painful experience, maximize the infant’s capacity for recovery, and activate their internal inhibitory control system. In addition, other sources of stress are present in an intensive care environment, which contribute to increase sensitivity of the neonates to future episodes of pain. To minimize the consequences of this harmful environment, especially in the most vulnerable babies, premature and/or those with a serious clinical situation, there are validated recommendations for special care to wherever possible prevent pain, family’s empowerment for comfort care, and support. Pain management is not just about administering a medication or another treatment, but rather integrated approaches that reduce or block the nociceptive activity of the trauma associated with invasive procedures. To minimize the adverse effects, pain management in neonatal care units requires the use of effective pharmacological and non-pharmacological interventions. The selection of analgesic interventions by healthcare professionals will depend on the type of the procedure, as well as the clinical condition of the newborn.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".