The comparison of the effectiveness of lincocin® and azitro® in the treatment of covid-19-associated pneumonia: A prospective study
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The COVID-19 virus has spread rapidly around the world and there are many patients in multiple coun-tries. Great efforts have been made to find effective medications against the COVID-19. This study aims to compare the effectiveness of LINCOCIN® and AZITRO® in the treatment of COVID-19 associated pneumonia. A total of 24 hospitalized patients aged between 30-80 years who were admitted to the Tarsus Medical Park Hospital between February to March 2020 was included in the study. The patients were divided into LINCOCIN® and AZITRO® treatment groups. Bronchoalveolar-lavage PCR results were compared after treatment. The mean age was 58.4±15.4 years in the LINCOCIN® group and 59.1±16.6 years in the AZITRO® group. In the LINCOCIN® group, the rate of males was 66.7% and it was 58.3% in the AZITRO® group. There were no statistical differences in terms of age and gender between the groups. On the 6th day after starting treatment, negative bronchoalveolar PCR result was 83.3% in the LINCOCIN® group and 33.3% in the AZITRO® group. The negative bronchoalveolar PCR proportion was significantly higher in the LINCOCIN® group than in the AZITRO® group. LINCOCIN® usage may be more appropriate in the treatment of COVID-19 associated pneumonia. Further studies with a large sample size should clarify these results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".