A SYSTEMATIC INTEGRATIVE REVIEW OF INFANT PAIN ASSESSMENT TOOLS
Bibliographic record
Abstract
PURPOSE: To examine the issue of pain assessment in infants by acquiring all available published pain assessment tools and evaluating their reported reliability, validity, clinical utility, and feasibility. DESIGN AND METHODS: A systematic integrative review of the literature was conducted using the following databases: MEDLINE and CINAHL (through February 2004), and Health and Psychosocial Instruments, and Cochrane Systematic Reviews (through 2003). MeSH headings searched included "pain measurement," with limit of "newborn infant"; "infant newborn"; and "pain perception." SUBJECTS: Thirty-five neonatal pain assessment tools were found and evaluated using predetermined criteria. The critique consisted of a structured comparison of the classification and dimensions measured. Further, the population tested and reports of reliability, validity, clinical utility, and feasibility were reviewed. RESULTS: Of the 35 measures reviewed, 18 were unidimensional and 17 were multidimensional. Six of the multidimensional measures were published as abstracts only, were not published at all, or the original work could not be obtained. None of the existing instruments fulfilled all criteria for an ideal measure; many require further psychometric testing. CONCLUSIONS: When choosing a pain assessment tool, one must also consider the infant population and setting, and the type of pain experienced. The decision should be made after carefully considering the existing published options. Confidence that the instrument will assess pain in a reproducible way is essential, and must be demonstrated with validity and reliability testing. Using an untested instrument is not recommended, and should only occur within a research protocol, with appropriate ethics and parental approval. Because pain is a multidimensional phenomenon, well-tested multidimensional instruments may be preferable.
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 | 0.024 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".