Counseling and management of patients requesting subcutaneous contraceptive implants: proposal for a decisional algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it