Counseling and management of patients requesting subcutaneous contraceptive implants: proposal for a decisional algorithm
Bibliographic record
Abstract
Despite the easy access to contraception today, the rate of unintended pregnancies is still high because of scarce education among women on the methods available and of non-adherence to indications or discontinuation of the contraceptive method chosen. Adherence to contraception can be implemented through counseling programs intended to provide potential users with information regarding all contraceptive options available and to address women's concerns in line with their lifestyle, health status, family planning, and expectations. In here, we evaluate a multi-step decisional path in contraceptive counseling, with specific focus on potential users of long-acting release contraception etonorgestrel. We propose an algorithm about the management of possible issues associated with the use of subcutaneous contraceptive implant, with a special focus on eventual changes in bleeding patterns. We hope our experience may help out health-care providers (HCPs) to provide a brief but comprehensive counseling in family planning, including non-oral routes of contraceptive hormones. Indeed, we believe that a shared and informed contraceptive choice is essential to overcome eventual side-effects and to improve compliance, rate of continuation and satisfaction, especially with novel routes of administration.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".