Multitasking and the technical quality of the electrocardiogram.
Bibliographic record
Abstract
BACKGROUND: The electrocardiogram (ECG) is a powerful clinical tool for diagnosing cardiac abnormalities. Proper ECG data acquisition is essential because it allows physicians to interpret ECG results accurately and efficiently. This is especially important for patients with acute myocardial infarction, so that they can receive early treatment. As a result of multitasking, ECGs are acquired by two groups of personnel at the University of Alberta Hospital, Edmonton - ECG technologists and non-ECG technologists. OBJECTIVE: To evaluate the effectiveness and quality of ECG acquisition at the University of Alberta Hospital site. METHODS: All adult ECGs acquired at the University of Alberta Hospital site from January 1 to June 30, 2000 were assessed. An ECG was classified as unacceptable if it lacked demographics identifying the patient, and/or it was of such poor technical quality that the interpretation was compromised. RESULTS: Of 25,509 ECGs acquired during this period, 13,849 (54%) and 11,660 (46%) ECGs were acquired by ECG technologists and non-ECG technologists, respectively. Eleven ECGs (0.08%) acquired by the ECG technologists and 3683 ECGs (32%) acquired by the non-ECG technologists were of unacceptable quality. The technical cost spent on these unacceptable ECGs is approximately $100,000 a year at this institution. CONCLUSIONS: Multitasking has resulted in a high rate of unacceptable ECGs. There is a significant difference in the effectiveness and quality of ECG acquisition performed by ECG technologists and non-ECG technologists. Poorly acquired ECGs impede proper diagnosis for patients, subject the institution to potential medical legal consequences and add an unnecessary burden to the health care budget.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".