Are patient characteristics associated with the accuracy of hysterosalpingography in diagnosing tubal pathology? An individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND: Conventional meta-analysis has estimated the sensitivity and specificity of hysterosalpingography (HSG) to be 65% and 83%. The impact of patient characteristics on the accuracy of HSG is unknown. The aim of this study was to assess by individual patient data meta-analysis whether the accuracy of HSG is associated with different patient characteristics. METHODS: We approached authors of primary studies reporting on the accuracy of HSG using findings at laparoscopy as the reference. We assessed whether patient characteristics such as female age, duration of subfertility and a clinical history without risk factors for tubal pathology were associated with the accuracy of HSG, using a random intercept logistic regression model. RESULTS: We acquired data of seven primary studies containing data of 4521 women. Pooled sensitivity and specificity of HSG were 53% and 87% for any tubal pathology and 46% and 95% for bilateral tubal pathology. In women without risk factors, the sensitivity of HSG was 38% for any tubal pathology, compared with 61% in women with risk factors (P = 0.005). For bilateral tubal pathology, these rates were 13% versus 47% (P = 0.01). For bilateral tubal pathology, the sensitivity of HSG decreased with age [factor 0.93 per year (P = 0.05)]. The specificity of HSG was very stable across all subgroups. CONCLUSIONS: The accuracy of HSG in detecting tubal pathology was similar in all subgroups, except for women without risk factors in whom sensitivity was lower, possibly due to false-positive results at laparoscopy. HSG is a useful tubal patency screening test for all infertile couples.
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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 | 0.038 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".