Bibliographic record
Abstract
To the editor—I have several concerns regarding the article “Efficacy of Doxycycline for Mild-to-Moderate Community-Acquired Pneumonia in Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials” by Choi et al [1]. In the study by Biermann et al [2], 221 patients were entered in a double-blind comparative study. A total of 191 patients were evaluated, 104 with pneumonia and 87 with exacerbation of bronchitis. The rates of cure and side effects for patients with bronchitis and pneumonia are reported; however, the number of patients with pneumonia given doxycycline (55) and the number of patients given spiramycin (49) reported by Choi et al in Table 1 of their article are not found in the Biermann et al article. Furthermore, it appears that Choi et al took the pneumonia cure rate (84%) and adverse effects rate (20%) reported by Biermann et al and applied them to doxycycline but applied the bronchitis cure rate (75%) and adverse effects rate (24%) to spiramycin. Choi et al report several numbers from Biermann et al that were not found in the original publication, so it is necessary for Choi et al to substantiate those numbers to justify inclusion of the Biermann article in their meta-analysis.
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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 | 0.354 | 0.638 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".