Additional file 1 of Dental problems and chronic diseases in mentally ill homeless adults: a cross-sectional study
Bibliographic record
Abstract
Additional file 1: Table S1. Univariate description of the baseline characteristics of the study participants, AH/CS Toronto Site. Table S2. Summary of the Imputed model performed for the study variables with missing data, AH/CS, Toronto Site. Figure S1. Comparison of the distribution of the observed, imputed and completed datasets in the first 10 (1 to 10/100) imputed datasets for the BMI variable. Table S3. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the iron-deficiency anemia variable. Table S4. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the stomach or intestinal ulcer variable. Table S5. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the arthritis variable. Table S6. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the thyroid problem variable. Table S7. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the diabetes variable. Table S8. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the liver disease (other than hepatitis) variable. Table S9. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for heart disease. Table S10. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the kidney/bladder problems variable. Table S11. Comparison of the proportion in the observed, imputed and completed dataset in the first 10 imputed datasets for chronic bronchitis/emphysema variable. Table S12. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the migraine variable. Table S13. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the asthma variable. Table S14. Comparison of the proportion in the observed, imputed and completed dataset in the first 10 imputed datasets for the effect of stroke variable. Table S15. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the dental problems variable. Table S16. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the inflammatory bowel problems variable. Table S17. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the hypertension variable. Table S18. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the lifetime homelessness variable. Table S19. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the ethno-racial group variable. Table S20. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the education variable. Table S21. Comparison of the proportions in the observed, imputed and completed dataset in the first 10 imputed datasets for the smoking variable.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.573 | 0.030 |
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".