Electric Bed Design and Features for Next Generation of Bedside Nursing
Bibliographic record
Abstract
On Easter Sunday 2016, I was hospitalized for a week at the brief hospitalization unit of St- Eustache Hospital; An institution of which I am the chief of biomedical engineering since September 9, 2009. Beyond the great technological achievements that I benefited as a patient, I noticed a few dysfunctions from the point of view of the patient that I became. The purpose of this article is to suggest a better arrangement of medical devices at the bedside of the patient to improve his episode of care. Based on the premise that any medical device basically at the patient's bedside should be incorporated into the bed, if technologically possible, we propose the design of a first-generation intelligent medical bed, using current bedside concepts, embedded or not. Some improvements are also proposed for the accessibility of the patient to the controls of existing beds. The second generation will focus on patient communication with the nursing station. Thus, the patient call will be graded and interpreted to discriminate the regular calls to medical emergencies. The third generation will consider network communication and the incorporation of medical and pharmacological transactions from the bedside to the patient record via the bed that will be networked and the patient, geographically identifiable, in real time. One of the objectives of this article is to encourage healthcare professionals who would eventually become a client of the healthcare network to report their observations and thus contribute to alter the services from within.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".