124 The Effect of Picture Archiving and Communications Systems on the Diagnostic Performance of Pediatric Emergency Physicians
Bibliographic record
Abstract
Digital radiography, with hospital-wide accessibility is being implemented in many centres. No previous study has evaluated the impact of a Picture Archiving and Communications Systems (PACS) on the diagnostic ability of Pediatric Emergency Physicians (PEPs). To compare the accuracy of diagnostic interpretation of radiographs by PEPs before and after the introduction of a PACS. A before-and-after study was conducted. The before time period (Group PRE) was defined as Sept 2001, during which time only conventional radiographs were available. The after time period (Group POST) was defined as September 2002, when only digital radiographic studies were available. The consecutive medical records of all patients (0–18 years) who underwent radiological studies while they presented to an Emergency Department (ED) of a pediatric university-affiliated centre during both study periods were reviewed. Ultrasonographic, CAT, MRI and bone scan studies were excluded. The radiographic interpretation of the PEP, documented at the time of the ED visit, was compared to the pediatric radiologist's report, documented within 72 hours of the ED visit. Data were obtained via the ED Hospital Information System, the Radiological Information System and the medical records. The criterion validity (sensitivity, specificity, NPV, PPV, accuracy) (95% CI) of the PEPs' radiographic interpretations were measured. Data were available from 1644 out of the 1651 sets of conventional radiographs ordered by PEPs for the Group PRE and from 1430 out of the 1431 sets of digital radiographic studies for the Group POST. A similar distribution of types of studies was found among both groups. Both groups were similar for age, times of presentation, triage levels and admission rates. The prevalence of positive radiological studies as per the radiologists was 32.2% for Group PRE and 28.7% for Group POST. Overall the PEP's accuracy was 98.1% (94.6–100%) vs. 98.5% (87.5–100%), sensitivity 96.4% (94.5–97.8%) vs. 98.1% (96.2–99.2%), specificity 98.9% (98.1–99.4%) vs. 98.6% (97.7–99.3%), NPV 98.3% (97.4–99.0%) vs. 99.2% (98.5–99.7%) and PPV 97.7% (96.0–98.8%) vs. 96.6% (94.4–98.2%) for Group PRE vs. Group POST respectively. The proportion of false negatives (FN) was 1.2% (19/1644) vs. 0.6% (8/1430). Only 1 FN patient for each group required immediate follow-up for a missed diagnosis. Radiographs interpretations by PEPs of digital studies remain extremely accurate compared to conventional studies. Infrequently, a severe diagnosis (1/1430) was missed by the PEPs with the use of digital radiographs.
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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 | 0.024 | 0.231 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".