Bibliographic record
Abstract
This dataset contains text for over 7,000 case report sections in academic papers related to Post COVID-19 Condition (PCC), also known as Long COVID. It is comprised of three columns: 'Token,' 'entity_type,' and 'case_report_id.' There are over 3.4 million observations in total. Entity types are presented in CoNLL format and relate to sociodemographic, behavioral and selected biomedical dimensions relevant to PCC. There are 27 core entity types (53 in CoNLL): ‘Access_To_Care,’ ‘Age,’ ‘Condition,’ ‘Diet,’ ‘Disability,’ ‘Education,’ ‘Employment,’ ‘Exercise,’ ‘Family_Member,’ ‘Gender,’ ‘Geographic_Entity,’ ‘Housing,’ ‘Income,’ ‘Insurance_Status,’ ‘Language,’ ‘Marital_Status,’ ‘Mental_Health,’ ‘Race_Ethnicity,’ ‘Severity,’ ‘Sexual_Orientation,’ ‘Social_Support,’ ‘Spiritual_Beliefs,’ ‘Substance,’ ‘Treatment,’ ‘Vaccine,’ ‘Violence_Or_Abuse,’ and ‘O’ (non-entity). The entities were annotated with a fine-tuned BERT-base-uncased model, which was trained on a subset of 501 case reports and 3000 sets of synthetic sentences with diverse variations of each entity type. For reproducibility, an additional file with case report sections alone is included.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.078 |
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".