Classification of stillbirth by the International Classification of Diseases for Perinatal Mortality using a sequential approach: A 20‐year retrospective study from Thailand
Bibliographic record
Abstract
AIM: The International Classification of Diseases for Perinatal Mortality (ICD-PM) is a system for recording causes of perinatal death. In this system, placental pathology is considered a "maternal condition" and this category does not cover the spectrum of placental pathology that can impact on perinatal death. The aim of the study was to apply a wider spectrum of placental pathology as a separate parameter for classifying death in the ICD-PM. METHODS: All autopsy reports at a single institution over a 20-year period (2001-2020) were reviewed. Causes of stillbirth were analyzed in a sequential manner: step 1, clinical history and laboratory results; step 2, placenta; and step 3, autopsy; and classified at each step according to the ICD-PM. RESULTS: The review identified 330 cases, including 126 antepartum and 204 intrapartum deaths. Step 1 identified a cause in 176 (86%) intrapartum deaths and 64 (51%) antepartum deaths. The addition of placental pathology (step 2) changed the cause of death in 12% of cases, with causes now identified in 190 (93%) intrapartum and 89 (71%) antepartum deaths. Adding step 3 did not identify any additional causes of death. CONCLUSION: The accuracy of the ICD-PM classification is dependent on the data available. Placental pathology made a significant difference in assigning causes of death in our series, stressing the importance of placental examination. Determination of the cause of death based on clinical history and laboratory data alone may be inaccurate, and less useful for comparative studies and planning prenatal care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".