Additional file 1 of Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach
Bibliographic record
Abstract
Additional file 1: Table S1. Search query for data cohort. Table S2. Named entities. Fig. S1. Active learning for data annotation. Fig. S2. Task-specific Transformer model for named entities task. Table S3. Notations used in the paper. Fig. S3. IOB format by CRF layer. Table S4. Case study: Named entities extracted from the case report (case report text only). Fig. S4. Case study, Visual representation of named entities from the snippet of case report. Table S5. NER on a general case report [3]. Fig. S5. Dependency parsing. Figure S6: Relation between disease disorder (entity) and psychological condition (entity). Table S6. Natural language processing-based summary of COVID-19 cohort. Table S7. Benchmark datasets and methods. Table S8. Hyperparameter and best result value (values in parenthesis represent the parameter ranges tested). Table S9. High frequency named entities in case reports. Fig. S7. Hospitalization, ICU admission, and morality in COVID-19 patients with different age groups
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.751 | 0.165 |
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".