Evaluation of a programme for ‘Rapid Assessment of Febrile Travelers’ (RAFT): a clinic-based quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Fever in the returned traveller is a potential medical emergency warranting prompt attention to exclude life-threatening illnesses. However, prolonged evaluation in the emergency department (ED) may not be required for all patients. As a quality improvement initiative, we implemented an algorithm for rapid assessment of febrile travelers (RAFT) in an ambulatory setting. METHODS: Criteria for RAFT referral include: presentation to the ED, reported fever and travel to the tropics or subtropics within the past year. Exclusion criteria include Plasmodium falciparum malaria, and fulfilment of admission criteria such as unstable vital signs or significant laboratory derangements. We performed a time series analysis preimplementation and postimplementation, with primary outcome of wait time to tropical medicine consultation. Secondary outcomes included number of ED visits averted for repeat malaria testing, and algorithm adherence. RESULTS: From February 2014 to December 2015, 154 patients were seen in the RAFT clinic: 68 men and 86 women. Median age was 36 years (range 16-78 years). Mean time to RAFT clinic assessment was 1.2±0.07 days (range 0-4 days) postimplementation, compared to 5.4±1.8 days (range 0-26 days) prior to implementation (p<0.0001). The RAFT clinic averted 132 repeat malaria screens in the ED over the study period (average 6 per month). Common diagnoses were: traveller's diarrhoea (n=27, 17.5%), dengue (n=12, 8%), viral upper respiratory tract infection (n=11, 7%), chikungunya (n=10, 6.5%), laboratory-confirmed influenza (n=8, 5%) and lobar pneumonia (n=8, 5%). CONCLUSIONS: In addition to provision of more timely care to ambulatory febrile returned travellers, we reduced ED bed-usage by providing an alternate setting for follow-up malaria screening, and treatment of infectious diseases manageable in an outpatient setting, but requiring specific therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".