An approach to quantify <i>ortho</i>-phthalaldehyde contamination on work surfaces
Bibliographic record
Abstract
Ortho-phthalaldehyde (OPA) is used as a high-level disinfectant for reusable medical devices in healthcare settings. The ACGIH recently adopted a Threshold Limit Value-Surface Limit (TLV-SL; 25 µg/100 cm2) for OPA surface contamination to prevent induction of dermal and respiratory sensitization following dermal exposure. However, there is no current validated method to measure OPA surface contamination. This study aimed to develop a standardized approach for sample collection and quantitative determination of OPA from work surfaces for use in risk assessment practices. The reported method utilises readily available commercial wipes to collect surface samples coupled with direct detection of OPA via liquid chromatography time of flight mass spectrometry (LC-ToF-MS). This approach avoided complex derivatization steps commonly required for the analysis of aldehydes. Method evaluation was conducted in accordance with the Occupational Safety and Health Administration (OSHA) surface sampling guidelines. Overall recoveries of 25 µg/100 cm2 of OPA from stainless steel and glass surfaces were 70% and 72%, respectively. The reported LOD for this method was 1.1 µg/sample and the LOQ was 3.7 µg/sample. OPA remained stable on the sampling medium for up to 10 days, when stored at 4 °C. The method was demonstrated in a workplace surface assessment at a local hospital sterilising unit, successfully detecting OPA on work surfaces. This method is intended to supplement airborne exposure assessment and provide a quantitative assessment tool for potential dermal exposure. When used in conjunction with a thorough occupational hygiene program that includes hazard communication, engineering controls, and personal protective equipment, skin exposure and consequent sensitization risks in the workplace can be minimized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".