The Use of Lidocaine Spray and Paracervical Nerve Block in Pain Management During Intrauterine Device Insertion: A Literature Review
Bibliographic record
Abstract
Introduction: An intrauterine device (IUD) is an extremely reliable form of contraception with one of the highest satisfaction rates among users. Yet, in Canada the use of IUDs as primary contraception remains low. Women report that pain during IUD insertion dissuades them from selecting this form of contraception. Despite evidence that women experience discomfort during IUD insertions, standard pain control options remain unavailable. Objective: This literature review aims to evaluate the evidence for lidocaine spray and lidocaine paracervical nerve block in the management of pain during IUD insertion. A second objective is to assess if these techniques are suitable for outpatient visits. Methods: A search on PubMed was performed with key words and inclusion criteria to obtain articles pertaining to the use of both topical and injected lidocaine as pain management techniques during IUD insertion. Results: Three studies explored the benefits of lidocaine spray during IUD insertion and two studies assessed the effects of a lidocaine paracervical nerve block. Not only were both topical and injected forms of lidocaine effective in reducing pain during IUD insertion, these techniques were also effective in reducing pain during other procedural steps. Additionally, all participants in the studies tolerated the procedure well on an outpatient basis. Conclusion: Two effective pain management techniques have been identified in the literature. Lidocaine spray was found to be effective in reducing pain in parous women during multiple steps of IUD insertion. While a paracervical block was effective in reducing pain during IUD insertion in nulliparous women. Further, these interventions were tolerated well and suitable for outpatient clinics.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".