Profiles of Independent-Comorbidity Groups in Senior COVID-19 Patients Reveal Low Fatality Associated with Standard Care and Low-Dose Hydroxychloroquine over Antivirals [Response to Letter]
Bibliographic record
Abstract
Kamaleldin B Said,1,2 Ahmed Alsolami,3 Fayez Saud Alreshidi,4 Anas Fathuddin,5 Fawwaz Alshammari,6 Fauwaz Alrashid,7 Ahmed Aljadani,3 Rana Aboras,4 Fatmah Alreshidi,4 Mohammed H Alghozwi,1 Suliman F Alshammari,1 Nawaf F Alharbi1 1Department of Pathology and Microbiology, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 2Genomics, Bioinformatics and Systems Biology, Carleton University, Ottawa, ON, K1S 5B6, Canada; 3Department of Internal Medicine, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 4Deparmtent of Family, Community Medicine, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 5Department of Plastic Surgery, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 6Department of Dermatology, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia; 7Department of Surgery, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi ArabiaCorrespondence: Kamaleldin B Said, Department of Pathology and Microbiology, College of Medicine, University of Ha’il, Ha’il, 55476, Saudi Arabia, Tel +966500771459, Email kbs.mohamed@uoh.edu.sa
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".