Lidocaine-Prilocaine Cream as Analgesia for IUD Insertion: A Prospective, Randomized, Controlled, Triple Blinded Study
Bibliographic record
Abstract
INTRODUCTION: Copper IUD is a long term and reversible contraception which equals tubal ligation in terms of sterilization. One of the barriers to using this contraception method is the fear and the pain associated with its insertion. Eutectic mixture of local anesthetics (EMLA) 5% is a local anesthetic that contains 25 mg lidocaine and 25 mg of prilocaine per gram. Application of topical analgesic cream to the cervix for laser surgery, hysteroscopy and hysterosalpingography is known Aims: this study aimed to determine the effect of EMLA on IUD insertion pain. METHODS: This triple blind clinical trial was conducted on 92 women in a clinic in Hamedan in 2012. After applying the cream on the cervix, pain in three steps, after using Tenaculum, after inserting hystrometr and after inserting IUD and removing IUD insertion tube were assessed with visual analog scale and were compared in EMLA group and placebo group Statistical analysis used to determine and compare the pain of independent t tests, Mann-Whitney U test and repeated measures analysis of variance and chi-square tests to determine the homogeneity of variables and Fisher's exact test was used. RESULTS: Insertion hystrometr was determined as the most painful IUD insertion. The mean pain at step 2 (inserting hystrometr) was (3.11±2.53) in EMLA group, (5.23±2.31) in placebo group. EMLA cream significantly reduced the pain after using tenaculum (P<0.001), pain inserting Hystrometr (P< 0.001) and pain at IUD insertion and removing insertion tube (P< 0.001) CONCLUSIONS: Topical Application of EMLA 5% cream as a topical anesthetic on the cervix before insertion IUD reduced the pain during this procedure.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".