Intrauterine device insertions: which variables matter?
Bibliographic record
Abstract
OBJECTIVES: Many factors are suspected to influence the intrauterine device (IUD) insertion process. This study sought to examine the effects of a few key variables on IUD insertion tolerability, complications and follow-up issues. METHODS: A retrospective chart review was undertaken of all IUD insertions over an 11-month period at an urban Canadian women's clinic. Linear regression analysis of 354 insertions assessed the effects of parity, age, local anaesthesia and other variables on insertion pain. Chi square (χ(2)) and analysis of variance statistics were employed to evaluate differences in insertion complications by parity and age. A χ(2) test was performed to compare follow-up expulsion or removal rates by parity. RESULTS: Statistically significant differences were found, with nulliparous women and those who received local anaesthesia reporting more insertion pain. Age, IUD type and recent abortion status did not affect insertion pain. Nulliparous women did not experience significantly more insertion difficulty or complications, nor did they have higher rates of expulsion or removal at follow-up. CONCLUSIONS: These findings suggest that the practice of providing cervical anaesthesia at IUD insertion may cause slightly more pain, without any obvious additional benefit. The difference in insertion pain by parity, although statistically significant, is small enough to be of questionable clinical importance. Overall, these findings add to the growing body of evidence for IUDs being safe and well-tolerated in nulliparous women.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".