DECONTAMINATION INTERVENTIONS FOR THE REUSE OF SURGICAL MASK PERSONAL PROTECTIVE EQUIPMENT (PPE): A PROTOCOL FOR A SYSTEMATIC REVIEW
Bibliographic record
Abstract
As the global spread of novel coronavirus (SARS-CoV-2) escalates, the high demand for personal protective equipment (PPE) has created shortages of this equipment globally and prompted the need to ensure appropriate stewardship and develop strategies to conserve supply. Surgical masks have broad and commonplace applications as PPE, including in the care of suspected or confirmed COVID-19 patients, and for the care of the general patient population in areas where community spread of COVID-19 is of concern. Surgical mask rationing and conservation is therefore a priority to ensure adequate supply during a pandemic. Several methods have been considered to decontaminate and allow the reuse of single-use PPE, such as hydrogen peroxide vapour and ultraviolet germicidal irradiation, but to date this literature has not been comprehensively synthesized. The objective of this systematic review is to identify and synthesize data from published studies evaluating interventions used to decontaminate or treat surgical mask PPE for the purposes of reuse.
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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 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".