Analysis of Articles on Neonatal Pain Published from 1965 to 1999
Bibliographic record
Abstract
OBJECTIVE: To analyze articles on neonatal pain indexed in MEDLINE from 1965 to 1999. METHODS: Papers published since 1965 and indexed in Medline (PubMed ) were considered. The search strategy was: 'pain' (MeSH terms) OR 'pain' (Title word) OR 'pain' (All Fields), AND 'infant, newborn' (MeSH terms). After the search was completed, a printed copy of the references was obtained, and each reference was reviewed to establish its pertinence to the study's objective. For each article, the year of publication, the journal of publication, the language of the journal, the country of the first author, the type of article and the subject were analyzed. RESULTS: Only 545 of 2490 references identified by the MEDLINE search were considered to deal with neonatal pain. Few articles on neonatal pain were published from 1965 to 1985, but the number of articles published on this topic increased sharply after 1985. Most of the papers were published in English (84.6%). The United States, Canada and the United Kingdom were the main countries that published neonatal pain articles (38.6%, 14.2% and 13.3%, respectively). Clinical research articles (31.7%) were published most frequently; the main subjects of all the articles were pain related to colic (20.4%), general neonatal pain issues (13.6%) and procedural pain (13.4%). Pediatrics and Archives of Diseases in Childhood were the journals that published the most articles on neonatal pain (27 and 22, respectively). CONCLUSIONS: Pain in neonates was a neglected subject of publication until the mid-1980s, and, currently, only a few countries seem interested in this type of pain. This lack of interest may be related to the undertreatment of pain in the neonatal period.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.060 | 0.063 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".