Temporal and external validation of the algorithm predicting first trimester outcome of a viable pregnancy
Bibliographic record
Abstract
BACKGROUND: Symptoms like vaginal bleeding or abdominal pain in early pregnancy can create anxiety about potential miscarriage. Previous studies have demonstrated ultrasonographic variables at the first trimester transvaginal scan (TVS) which can assist in predicting outcomes by 12 weeks gestation. AIM: To validate the miscarriage risk prediction model (MRP) in women who present with a viable intrauterine pregnancy (IUP) at the primary ultrasound. MATERIALS AND METHODS: A multi-centre diagnostic study of 1490 patients was performed between 2011 and 2019 for retrospective external and 2017-2019 for prospective temporal validation. The reference standard was a viable pregnancy at 12 + 6 weeks. The MRP model is a multinomial logistic regression model based on maternal age, embryonic heart rate, logarithm (gestational sac volume/crown-rump length (CRL)) ratio, CRL and presence or absence of clots. RESULTS: Temporal validation data from 290 viable IUPs were collected: 225 were viable at the end of the first trimester, 31 had miscarried and 34 were lost to follow-up. External validation data from 1203 viable IUPs were collected at two other ultrasound units: 1062 were viable, 69 had miscarried and 72 were lost to follow-up. Temporal validation with a cut-off of 0.1 demonstrated: area under the curve (AUC) of 0.8 (0.7-0.9), sensitivity 66.7%, specificity 83.9%, positive predictive value (PPV) 35.7%, negative predictive value (NPV) 94.9%, positive likelihood ration (LR+) 4.1 and negative LR (LR-) 0.4. External validation demonstrated: AUC 0.7 (0.7-0.8), sensitivity 44.9%, specificity 90.4%, PPV 23.3%, NPV 96.2%, LR+ 4.6 and LR- 0.6 (0.4-0.7). CONCLUSION: The MRP model is not able to be used in real time for counselling, and management should be individualised.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".