Multisociety guidance for sterilization and high-level disinfection
Bibliographic record
Abstract
This multisociety infection prevention guidance document was developed for individuals and organizations that engage in sterilization or high-level disinfection (HLD). It was endorsed by SHEA, APIC, ASGE, IDSA, and SGNA. This guidance is based on a synthesis of published scientific evidence, theoretical rationale, current practices, practical considerations, writing group consensus, and consideration of potential harm when applicable. The supplementary material includes a summary of recommendations. The guidance provides an overview of the Spaulding Classification and considerations around manufacturers' instructions for use (MIFUs). Its recommendations address: point-of-use treatment prior to sterilization or HLD, preparation of reusable medical devices at the location of processing, sterilization, and immediate use steam sterilization (IUSS), HLD of lumened and non-lumened devices, processing of reusable medical devices used with lubricating or defoaming agents, monitoring for effectiveness of processing, handling of devices after HLD, augments and alternatives to HLD, processing of investigational devices, tracking of reusable medical devices, and approaches to implementation.
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 | 0.036 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.067 | 0.064 |
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".