The TransFLUas influenza transmission study in acute healthcare - recruitment rates and protocol adherence in healthcare workers and inpatients
Bibliographic record
Abstract
BACKGROUND: Detailed knowledge about viral respiratory disease transmission dynamics within healthcare institutions is essential for effective infection control policy and practice. In the quest to study viral transmission pathways, we aimed to investigate recruitment rates and adherence of healthcare workers (HCWs) and hospital inpatients with a study protocol that involves prospective surveillance based on daily mid-turbinate nasal swabs and illness diaries. METHODS: Single center prospective surveillance of patients and HCWs in three different hospital departments of a tertiary care center during an entire influenza season in Switzerland. Inpatients and acute care HCWs were asked to provide mid-turbinate nasal swabs and illness diaries on a daily basis. Study protocol adherence and recruitment rates were the primary outcomes of interest. RESULTS: A total 251 participants (59 (23.5%) health care workers and 192 (76.5%) inpatients) were recruited from three different hospital wards. Recruitment rates differed between HCWs (62.1% of eligible HCWs) and inpatients (32.5%; P < 0.001), but not within HCWs (P = 0.185) or inpatients (P = 0.301) of the three departments. The total number of study-days was 7874; 2321 (29.5%) for inpatients and 5553 (70.5%) for HCWs. HCWs were followed for a median of 96 days (range, 71-96 days) and inpatients for 8 days (range, 3-77 days). HCWs provided swabs on 73% (range, 0-100%) of study days, and diaries on 77% (range 0-100%). Inpatients provided swabs and diaries for 83% (range, 0-100%) of days in hospital. In HCWs, increasing age, working in internal medicine and longer duration of total study participation were positively associated with the proportion of swabs and diaries collected. Adherence to the study protocol was significantly lower in physicians as compared to nurses for both swabs (P = 0.042) and diaries (P = 0.033). In inpatients, no association between demographic factors and adherence was detected. Conclusions Prospective surveillance of respiratory viral disease was feasible in a cohort of inpatients and HCWs over an entire influenza season, both in terms of recruitment rates and adherence to a study protocol that included daily specimen collection and illness diaries. TRIAL REGISTRATION: clinicaltrials.gov NCT02478905 . Date of registration June 23, 2015.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".