Additional file 1 of Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Table S1. Search strategies applied in each bibliographic database. Table S2. Characteristic of studies included in the present study for analyzing risk of incident diabetes post-COVID-19 versus matched controls. Table S3. Quality assessment of included studies with the Newcastle–Ottawa Scale. Figure S1. Subgroup analysis of diabetes type. Figure S2. Subgroup analysis to whether the control group was or was not upper respiratory tract infections. Figure S3. Forest plot showing the incidence of DM among different age groups after COVID-19. Figure S4. Subgroup analysis of different age groups. Figure S5. Forest plot showing the incidence of DM among different gender after COVID-19. Figure S6. Subgroup analysis of gender. Figure S7. Forest plot showing the incidence of diabetes among different follow-up time after COVID-19. Figure S8. Subgroup analysis of different follow-up time. Figure S9. Forest plot showing the incidence of diabetes based on different levels of COVID-19 severity. Figure S10. Subgroup analysis of different levels of COVID-19 severity. Figure S11. Bias factor = log(1). Figure S12. Bias factor = log(2.08).
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 | 0.004 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.740 | 0.027 |
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".