Reproductive surgery in the 21st century
Bibliographic record
Abstract
The result of infertility treatment can be assessed accurately by the monthly fecundity rate and the cumulative pregnancy rate (CPR). The monthly fecundity rate, decreasing over time, and the time needed to reach the ultimate CPR are key factors in decision making. Depending on the clinical assessment, infertility treatment will be either with in vitro fertilization (IVF)/assisted reproduction technologies (ART) or with a diagnostic laparoscopy associated with reproductive surgery, which thereafter my require require IVF/ART. The comparison of IVF/ART treatment versus reproductive surgery is therefore the wrong debate as the CPR’s of reproductive surgery and of IVF are additive. Decisions should be based on the ultimate CPR’s and on effort and time, not on personal preferences. The large majority of women with infertility should have a diagnostic laparoscopy during which reproductive surgery can be performed if needed. IVF/ART treatment without a diagnosis decreases the ultimate CPR and is not without potentially serious adverse effects. Having excellent reproductive surgery readily available to patients, similar to the availability of IVF would increase CPR in women with infertility and decrease the overall cost.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".