Hysterosalpingosonography for diagnosing tubal occlusion in subfertile women: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Hysterosalpingosonography has been suggested as a less invasive alternative to hysterosalpingography for detecting tubal occlusion among subfertile women. We aim to determine the diagnostic accuracy of hysterosalpingosonography and to compare it to hysterosalpingography. METHODS/DESIGN: We will conduct a systematic review of diagnostic test accuracy. We will search Medline, Embase, Cochrane Library, Web of Science and Biosis, as well as reference lists of included studies and previous related review articles. Diagnostic studies that compared hysterosalpingosonography ± hysterosalpingography to laparoscopy with chromotubation in women suffering from subfertility will be eligible. Two authors will independently screen for inclusion, data extraction, and quality assessment. Methodological quality will be assessed using the Quality Assessment of Diagnostic Accuracy Study 2 tool (QUADAS-2). We will use SAS 9.3 (SAS Institute Inc., Cary, NC, USA, 2011) to program bivariate random-effects models, estimate pooled sensitivity and specificity with 95% confidence intervals and to generate summary receiver operating characteristics curves. We will perform sensitivity analyses to examine the effect of differences in techniques used for hysterosalpingosonography and in methodological quality of studies. DISCUSSION: This systematic review will help to determine if hysterosalpingosonography is an adequate alternative screening test for diagnosing tubal occlusion. Accuracy of specific sono-HSG techniques may also be identified. TRIAL REGISTRATION: This review has been registered at PROSPERO. The registration number is CRD42013003829.
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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 | 0.057 | 0.074 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".