The continuum of high ovarian response: a rational approach to the management of high responder patient subgroups
Bibliographic record
Abstract
Ovarian follicular responsiveness to controlled ovarian hyperstimulation (COH) with gonadotropins is extremely variable between individual patients, and even from cycle to cycle for the same patient. High responder patients are characterized by an exaggerated response to gonadotropin administration, accompanied by a higher risk for ovarian hyperstimulation syndrome (OHSS). In spite of its importance, the literature regarding high responders is characterized by heterogeneous classification methodologies. A clear separation should be drawn between risk factors for a high ovarian response and the actual response exhibited by a patient to stimulation. Similarly, it is important to distinguish between high ovarian response and development of clinically significant OHSS. In this article we: (1) review recent publications pertaining to the identification and clinical management of high responders, (2) propose an integrated clinical model to differentiate sub-groups within this population based on this review, and (3) suggest specific protocols for each sub-group. The model is based on a chronological patient assessment in an effort to target treatment based on the specific clinical circumstances. It is our hope that the algorithm we have developed will assist clinicians to supply targeted and precise treatments in order to achieve a favorable reproductive outcome with minimum complications for each patient.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".