Bibliographic record
Abstract
It seems that we hear news reports of disasters in IVF clinics almost weekly. Public concern over these reports has resulted in governments introducing regulation of IVF labs around the world, and within our profession there is a growing recognition of the need for accreditation of IVF labs to ensure that the potential for such errors occurring is minimized. Quality systems, which have an inherent role in all modern accreditation schemes, are essentially based on the principles of ISO 9000 and related standards. Yet quality management beyond basic assay quality control is often poorly understood by biomedical scientists, especially outside clinical chemistry and pathology laboratories. In particular, risk analysis and minimization are being demanded of IVF labs, but many IVF scientists have only limited understanding of how to go about these tasks. Perhaps this is because the majority of scientists working in clinical IVF labs have come from academic/research backgrounds and, as a consequence, many have limited experience of the practicalities of laboratory management – and even fewer have any formal training in it. Certainly IVF has evolved rapidly over the last two-and-a-half decades or so: from its beginnings as a highly experimental procedure in the late 1970s, culminating in the birth of Louise Brown on 25 July 1978 (Edwards and Steptoe, 1980), to a rapidly expanding field of research and clinical practice that swept the world in the 1980s and was consolidated as a routine clinical service in the 1990s.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.366 | 0.204 |
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".