Antibodies production in gender groups for surviving COVID-19.
Bibliographic record
Abstract
Objectives: A pilot study was executed in the month of June- July 2020 in Qazi Hussain Ahmed Medical complex Nowshera with aim to determine the gender protective role in term of production of neutralizing Anti SARS-COV-2 antibodies. Material and methods: A total of 39 COVID-19 patients who recovered from COVID-19 were selected. Their antibodies cut off values were measured by electro-chemiluminescence immunoassay using Roche Cobas E411 Chemistry Analyzer for which commercial kits of Roche diagnostics were used as per the instructions of the manufacturer. Results: A statistically significant difference in mean post infection antibodies level was observed with higher cut off values in patient who had symptoms at time of being reported positive by PCR as compared to patient who were asymptomatic (p-value:0.04). Using Kaplan Meir it was predicted that in female gender, the probability of survival is 100% at cut off antibodies levels of 50, While a vertical drop up to less than 40% of probability of survival was predicted in male gender even at higher antibodies levels of >100, that supports the prediction of production of higher levels of antibodies in female gender in early infections. Conclusion: The female gender produces higher titer of antibodies in early infections to confer immunity in COVID-19.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".