Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Brief Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities. Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities.
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.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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 it