Preclinical Evaluation of a Task-Shifting Contraceptive Implant Insertion Device for Use in Low- and Middle-Income Countries
Bibliographic record
Abstract
Abstract Worldwide, 225 million women have unmet contraceptive needs which, every year, leads to 52 million unintended pregnancies. A challenge to providing universal access to contraception is the large proportion of the population living in rural, difficult-to-access settings in low- and middle-income countries. Further, the availability and delivery of effective contraception in rural areas are limited by the lack of trained healthcare providers required to administer long-acting reversible contraceptives. In this study, we describe the design and testing of the SubQ Assist, a task-shifting contraceptive implant insertion device that aims to ensure appropriate and consistent subdermal administration. Cadaver testing and ultrasound depth measurements were used to evaluate the performance of the SubQ Assist. Implant insertion outcomes between the SubQ Assist operated by a trained clinician and the conventional freehand insertion method by a trained clinician were compared. Cadaver testing and ultrasound depth measurements demonstrated that the SubQ Assist resulted in implant insertions that were statistically equivalent to implants inserted using the freehand insertion method. Additionally, the results showed that the lateral positioning of these implants would facilitate uncomplicated removal at a later date. These findings demonstrated that the SubQ Assist can consistently insert implants at an appropriate depth. Pending clinical testing, the SubQ Assist may be an effective method of task-shifting the insertion of contraceptive implants from providers with advanced skills to minimally trained providers in order to expand access in rural areas.
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How this classification was reachedexpand
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 | 0.011 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".