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John G Thomas

Publications and source records attributed to John G Thomas.

3 recordsLinked to original sources

Forward and backward recall: different response time patterns, same retrieval order.

How do people retrieve information in forward and backward recall? To address this issue, we examined response times in directional recall as a function of serial position and list length. Participants memorized lists of four to six words and entered responses at the keyboard. Recall direction was postcued. Response times exhibited asymmetry in terms of direction. In forward recall, response times peaked at the first position, leveling off for subsequent positions. Response times were slower in backward recall than in forward recall and exhibited an inverse U-shaped function with an initial slowdown followed by a continuous speedup. These asymmetries have implications for theoretical models of retrieval in serial recall, including temporal-code, rule-based, and network models. The response time pattern suggests that forward recall proceeds in equal steps across positions, whereas backward recall involves repeated covert cycles of forward recall. Thus, retrieval in both directions involves a forward search.

Adolescent↗

Database-driven computerized antibiotic decision support: novel use of expert antibiotic susceptibility rules embedded in a pathogen-antibiotic logic matrix.

To better serve an antibiotic guidance program, we hypothesized that the relatively few antibiotic susceptibility measurements conducted in the microbiology laboratory could be extended to predict antibiotic susceptibilities for all antibiotics on the hospital formulary using expert infectious disease logic. With the assistance of infectious disease specialists, we developed these logic rules and then applied them to 26,196 unique patient culture specimens and the accompanying 334,131 antibiotic susceptibility measurements generating 804,809 additional predicted bug-drug susceptibility data points. From the resulting data set, the antibiotic susceptibility profile for one pathogen, Streptococcus pneumoniae, is highlighted herein. We then incorporated the extended susceptibility profiles into a computerized antibiotic guidance program that matches current patients of interest with the positive cultures from past similar patients and calculates predicted effective antibiotic therapy. We conclude that this method successfully derives antibiotic predictions and merits further testing to evaluate its potential use in the hospital environment.

Anti-Bacterial Agents↗

Computerized antimicrobial decision support for hospitalized patients with a bloodstream infection.

We developed a computerized antimicrobial decision support program founded on our local bacterial susceptibility data. In a retrospective analysis of patients with a bloodstream infection, we compared the actual antimicrobials prescribed to the antimicrobials recommended by the program. We found the computer-guided therapy to be clinically and statistically more effective than the therapy initiated by the physicians. We conclude that computerized decision support can improve the targeting of empiric antimicrobial therapy.

Anti-Infective Agents↗