Imaging delays among medical inpatients in Toronto, Ontario: A cohort study
Bibliographic record
Abstract
BACKGROUND: Imaging procedures are commonly performed on hospitalized patients and waiting for these could increase length-of-stay. The study objective was to quantify delays for imaging procedures in General Internal Medicine and identify contributing patient, physician, and system factors. METHODS: This was a retrospective cohort study of medical inpatients admitted to 5 hospitals in Toronto, Ontario (2010-2019), with at least one imaging procedure (CT, MRI, ultrasound, or peripherally-inserted central catheter [PICC] insertion). The primary outcome was time-to-test, and the secondary outcome was acute length-of-stay after test ordering. RESULTS: The study cohort included 73,107 hospitalizations. Time-to-test was longest for MRI (median 22 hours) and shortest for CT (median 7 hours). The greatest contributors to time-to-test were system factors such as hospital site (up to 22 additional hours), location of test ordering (up to 10 additional hours), the timing of test ordering relative to admission (up to 13 additional hours), and ordering during weekends (up to 21 additional hours). Older patient age, having more comorbidities, and residence in a low-income neighborhood were also associated with testing delays. Each additional hour spent waiting for a test was associated with increased acute length-of-stay after test ordering, ranging from 0.4 additional hours for CT to 1.2 hours for MRI. CONCLUSIONS: The greatest contributors to testing delays relate to when and where a test was ordered. Wait times affect length-of-stay and the quality of patient care. Hospitals can apply our novel approach to explore opportunities to decrease testing delays locally.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".