Additional file 1 of Doxycycline vs azithromycin in patients with scrub typhus: a systematic review of literature and meta-analysis
Bibliographic record
Abstract
Additional file 1: Supplementary Table 1. Databases searched and search string used for the systematic review. Supplementary Table 2. Inclusion and Exclusion criteria used for screening and full-text review. Supplementary Table 3. Summary of studies with data available for either doxycycline or azithromycin but not both. Supplementary Table 4. Additional details of patient selection criteria in terms of the use of diagnostics, and previous antimicrobial use. Supplementary Table 5. Fever, mortality, and adverse events related to primary and secondary outcomes in the included studies. Supplementary Figure 1. Meta-analysis of doxycycline vs. azithromycin for time to defervescence classified according to age group. Supplementary Figure 2. Meta-analysis to calculate the standardised mean difference of time to defervescence between doxycycline and azithromycin with studies stratified according to the severity.Supplementary Figure 3. Mean difference of time to defervescence between doxycycline and azithromycin categorised according to whether loading dose was given or not. Supplementary Figure 4. Meta-analysis of doxycycline vs azithromycin showing the proportion of patients not achieving defervescence within five days of initiation of drugs. Supplementary Figure 5. Meta-analysis of doxycycline vs. azithromycin showing the proportion of patients with treatment-related adverse effects.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.010 |
| 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) | 0.664 | 0.019 |
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".