Effect of infrared belt and hot water bag on labor pain intensity among primiparous: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Labor pain is complex, paradoxical and varied in every parturient woman. Management of labor pain has been a crucial component in maternity care. Heat therapy is one of the proposed method for labor pain relief. Infrared is one of the methods of heat therapy but there is any study in this regard. This study aimed to compare the effect of the infrared belt and hot water bag on the severity of pain in the first stage of labor among primiparous women. METHODS: In this clinical trial in the first stage of labor, 20-min cycles of heat therapy were conducted at the dilations of 4-5 and 6-7 cm in the intervention group 1 by an infrared belt and in the intervention group 2 by hot water bag, respectively. The control group received routine care. The severity of the pain was measured by the short-form McGill Pain Questionnaire. RESULTS: In total, 136 women consented to participate in this clinical trial study. The mean score of pain intensity was significantly lower in the two intervention groups compared to the control group (P < 0.001). The mean pain intensity was significantly lower in the infrared belt group than in the hot water bag group (P < 0.001). CONCLUSIONS: Based on these findings, heat therapy with an infrared belt reduced the severity of pain in the first stage of labor. The infrared belt could be used and recommended as a safe and effective pain relief in childbirth and maternity care. TRIAL REGISTRATION: This study was registered in the Iran Clinical Trial Center with the code of IRCT20190805044446N1 .
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".