Bibliographic record
Abstract
Context Mechanical “aneroid sphygmomanometers” (ASMs) continue to have a role in blood pressure assessment. Unfortunately, ASM components can deform with use, leading to inaccuracy. Although manufacturers recommend annual ASM retesting, this rarely occurs, potentially leading to errors in hypertension diagnosis/treatment. There are no modern-day assessments of calibration for the aging ASMs in community medical clinics, and no data to guide physicians on how often retesting is needed. Objective To evaluate the calibration of ASMs in primary care clinics and examine correlations between miscalibration and device age and usage. Study Design and Analysis Volunteer primary care clinics across Alberta, Canada underwent site visits to assess calibration of all clinic ASMs. Clinicians additionally estimated ASM age and total inflations, and provided calibration history. Degree of miscalibration was presented graphically – broken down by median splits of ASM age, and total inflations. Setting 25 primary care clinics widely-distributed across the province of Alberta, Canada from June to July 2024. Population Studied Assessment of all ASMs utilized by clinic physicians. Instrument A standardized reference manometer (a SPER Scientific 30PSI manometer model 840082 with Jan 5 2024 certificate of calibration) was used to measure ASM calibration. Outcome Measures Percentage of ASMs miscalibrated by 5-10 mmHg, and >10 mmHg, at pressures of 50, 100, 150 and 200 mmHg. Results We assessed 288 ASMs used by 216 physicians. Median ASM age was 11 years (range: 4 months to 40 years), and median number of inflations was 12,480 (range: 0 to 50,960). Miscalibrations of 5-10 mmHg were observed in 1.0% of devices at 50 mmHg, 0.7% at 100 mmHg, 1.0% at 150 mmHg, and 2.1% at 200 mmHg. For miscalibration exceeding 10 mmHg, the proportions were 0% at 50 and 100 mmHg, and 0.3% at 150 and 200 mmHg. Age had no influence on the likelihood of miscalibration, while a higher number of inflations (>12,480) appeared to increase the likelihood of miscalibration slightly. Conclusions The vast majority of ASMs were calibrated within 5 mmHg across all pressures, suggesting ASMs in primary care settings are generally reliable, possibly due to reduced frequency of use following the introduction of automated blood pressure monitors. Over a typical 40-year primary care career, newly purchased ASMs are unlikely to require replacement if used infrequently, alongside an automated device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".