Frequency of and variation in low-value care in primary care: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Low-value care, defined as care with a lack of benefit, can lead to higher health care costs, inconvenience to patients and, in some cases, harm to patients. The objectives of this study are to conduct exploratory analyses to understand how frequently selected low-value tests are ordered, to assess the degree of variation in ordering that exists across regions and practices, and to identify services that may warrant further investigation and targeted interventions. METHODS: We conducted a population-based retrospective cohort study using administrative health care databases from Ontario to identify rates of use of the following low-value services between fiscal years 2008/09 and 2012/13: computed tomography (CT) or magnetic resonance imaging (MRI) after a diagnosis of low back pain, Papanicolaou testing in women less than 21 years of age or older than 69 years of age and repeated dual-energy X-ray absorptiometry (DEXA) scanning within 2 years of an index scan. Regional and practice-level rates were calculated. Bivariate analyses were conducted to explore associations between patient factors and repeat DEXA scans. RESULTS: Repeated DEXA scans were the most common service (21.0%), whereas cervical cancer screening among women less than 21 years of age or older than 69 years of age (8.0%) and CT or MRI imaging for low back pain (4.5%) were less common. There was substantial variation across practices with rates of repeated DEXA scans, ranging from 4.0% to 54.9%, and cervical cancer screening, ranging from 0.9% to 35.2%. Patients with a high-risk index DEXA were more likely to receive a repeat scan (28.1%) than those with a baseline (8.9%) or low-risk (8.1%) scan. INTERPRETATION: There is significant, practice-level variation in the frequency of low-value testing for DEXA scans, back imaging and cervical cancer screening. There is a particular need for interventions that aim to reduce unnecessary DEXA scans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".