Performance Analysis of a Medical Decision Algorithm to Mitigate Spread of SARS Due to Interfacility Patient Transfers
Bibliographic record
Abstract
OBJECTIVE: To determine performance of a medical decision algorithm to mitigate spread of severe acute respiratory syndrome (SARS) from interfacility patient transfers during the Toronto SARS outbreak. METHODS: Records from the Provincial Transfer Authorization Centre and Toronto Public Health from April 1 to July 31, 2003, were linked using probabilistic methods. Authorization decision (transfer authorized or denied) and SARS status (probable case, suspect case, or patient under investigation for SARS; or non-SARS case) were obtained for linked records. Primary outcome was the number of patients where correct authorization decisions were made based on SARS status at the time of request. Secondary outcome was the number for whom, in retrospect, authorization decision was correct knowing final SARS status. Algorithm sensitivity, specificity, and predictive values were determined. RESULTS: There were 14,571 requests for transfer and 2,132 patients investigated for SARS during the study period. The algorithm authorized 14,551 and did not authorize 20 requests. Sensitivity and specificity to make appropriate authorization decisions at the time of request were 100% (95% confidence interval [CI], 77.2%-100%) and 99.95% (95% CI, 99.9-100%), respectively. Positive and negative predictive values were 65% (95% CI, 44.1%-85.9%) and 100% (95% CI, 98.4%-100%), respectively. Sensitivity and specificity, in retrospect, within ten days of the transfer request were 100% (95% CI, 80.6%-100%) and 99.97% (95% CI, 99.9%-100%), respectively. Positive and negative predictive values were 80% (95% CI, 62.5%-97.5%) and 100% (95% CI, 98.4%-100%), respectively. Seven of the 20 patients with nonauthorized requests were not known to have SARS at the time of request. Within ten days, three of seven were under investigation for, a suspect case of, or a probable case of SARS. CONCLUSIONS: The medical decision algorithm was highly sensitive and specific in correctly authorizing transfers. Despite its highly sensitive and specific algorithm, it did incorrectly deny authorization to a very small number of patients without SARS.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".