Fertility History: Assessment of Performance and Quality Improvement
Bibliographic record
Abstract
Background: Many fertility clinics use their own standard history sheet. Currently, no standard history sheet is in use in our unit. This audit project aimed at assessing the quality of the history obtained from couples with sub-fertility without using a standard history sheet followed by designing and introducing a standardized history sheet and re-auditing the quality of the history obtained to assess if introducing a standardized history sheet into our unit will improve the quality of the service provided. Methods: We started by designing a standardized history sheet which is simple, non-time consuming and contains all the basic information required at that very early stage of managing couples with sub-fertility. Data were collected from 50 notes retrospectively where we made sure that the notes included were for patients seen by different registrars at their first consultation. The standardized sheet was then introduced and all doctors were asked to use it for history taking during the first consultation with new couples seen in the clinic. Data were then collected from 50 notes where the standardized sheet was used. Results: The quality of the history taken at the first consultation is inconsistent and variable. The quality may be improved if a simple form of standard history sheet is used by all doctors doing the fertility clinic. Conclusion: History taking is a fundamental step in the management of sub-fertile couples. Couples complaining of sub-fertility will have their history taken during their first consultation at the reproductive medicine clinic. As the gynecology trainees running the clinic can be alternating, the quality of the history taken from these patients can vary. Obtaining accurate and complete information will help in making the management of those patients a smoother process with reducing the number of consultations and increasing patient satisfaction. J Clin Gynecol Obstet. 2016;5(3):77-80 doi: http://dx.doi.org/10.14740/jcgo411w
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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 | 0.048 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".