Bibliographic record
Abstract
To the Editor, Martinez Lamas et al. (2020) provide some preliminary findings on the potential use of povidone-iodine for reducing oropharyngeal viral load of SARS-CoV-2. Chin et al. (2020) have also proposed some efficacy for the antiviral properties of povidone-iodine in another context. Both of these findings offer some promise for implementing povidone-iodine as a tool for prevention. Their findings are also consistent with the use of halogens generally as coronavirus antivirals (Cimolai, 2020a). The use of RT-PCR as the tool to assess viral load nevertheless has some potential limitations. Although clinical samples are often extracted prior to amplification, a number of inhibitors may be present that may not be removed sufficiently. These inhibitors can be of a variety of chemicals or natural substances and must be controlled for in these assays (Schrader, Schielke, Ellerbroek, & Johne, 2012). While povidone-iodine may be the main ingredient in the specific mouthwash preparation, there are often a number of unlisted ingredients (e.g., alcohol) which can potentially provide both antisepsis and RT-PCR inhibition. Povidone (polyvinylpyrrolidone, a polymer) in itself can inhibit the polymerase chain reaction under varied circumstances (Koonjul, Brandt, Farrant, & Lindsey, 1999). Assessments of viral load with this method therefore require some controls. As another form of control, however, it would be of relevance to see what the lavage properties of gargling in itself have for reducing viral load in those samples assessed. Such a control could include saline or the carrier vehicle-minus povidone-iodine in the least. Furthermore, it is well-known that although there may be a correlation between RT-PCR values and quantitation of virus, the relative values obtained do not guarantee the absence of live virus which may be responsible for transmission (Cimolai, 2020b; Walsh et al., 2020). In the haste to find some efficacious prevention and treatment, some may be lulled into the use of such products without these validations. The use of controls as detailed above provides a measure of scientific rigor that will fortify the potential of such preventative products for their efficacy and rightful clinical applications. There are no conflicts of interest. Nevio Cimolai: Conceptualization; Formal analysis; Validation.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".