Bibliographic record
Abstract
Smartphone-Based Eye Exam for the ED A new smartphone-based retinal camera makes it easier for physicians to perform eye exams in the ED and rural health care centers.Figure: smartphone, eye exam, ED, rural health care, FOP-NM handheld fundus camera, Remidio, non-mydriatic fundus camera, ETDRS, Medios DR, AI, UniDoc Health, DocBox, predictive analytics, Canadian Triage and Acuity Scale, CTAS, STI test, sexually transmitted infections, Visby Medical, point-of-care, sexual health test, pathogens, chlamydia, gonorrhea, trichomoniasisThe FOP-NM handheld fundus camera from Remidio, Inc., is the world's first smartphone-based non-mydriatic fundus camera, according to a company press release. (May 7, 2024; https://tinyurl.com/2n7dshud.) The camera is held a short distance from the eye and provides high-quality images without dilating the eye or discomforting the patient. The FOP-NM features internal fixation and automatically focuses. It also offers a wider field of view, allowing physicians to capture up to eight fields of the retina instead of the standard ETDRS seven-segment montaged image. The camera works with Medios DR, Remidio's AI algorithm, which can automatically detect signs of diabetic retinopathy in the retina in less than seven seconds without requiring an internet connection or teleophthalmology support. The camera is designed to be handheld, but it can also be mounted on a chinrest or a slit lamp with the help of a support bar. AI Program Predicts Patient Decline UniDoc Health Corp. is betting that AI predictive health technology can improve efficiency and patient care in the ED. UniDoc is expanding its partnership with DocBox, Inc., to integrate advanced monitoring and predictive analytics into ED operations, according to a company press release. (May 14, 2024; https://tinyurl.com/58zrm6s5.) Managing non- and less-urgent patients comprises more than 50 percent of ED visits. (CBC. Jan. 10, 2024; https://tinyurl.com/yrr6b3du.) UniDoc's AI predictive health technology uses advanced algorithms to forecast a potential decline in a patient's health and help EPs intervene before a situation escalates. That improves how EDs manage the flow of nonurgent patients and frees up resources for more severe cases. UniDoc will implement DocBox's monitoring systems, which are equipped to handle Canadian Triage and Acuity Scale (CTAS) levels 4 and 5 patients with real-time data analysis and alert capabilities. These nonurgent ED patients still require medical assessment and care, and this system aims to ensure that deviations in a patient's health are promptly addressed, allowing for immediate and appropriate medical responses. The predictive algorithms should also help EDs reduce unnecessary wait times and ease crowding. STI Test Leads to Shorter ED Visits A new test for sexually transmitted infections (STIs) in women shortens ED visits and improves treatment for patients, according to a press release from Visby Medical, which announced findings from a Johns Hopkins Department of Emergency Medicine study evaluating the new point-of-care test. (May 30, 2024; https://tinyurl.com/5ez8fnff.) The data were presented at the 2024 annual meeting of the Society for Academic Emergency Medicine. The study examined how the Visby Medical Sexual Health Test—a polymerase chain reaction test—managed chlamydia, gonorrhea, and trichomoniasis. These pathogens, if left untreated, can cause permanent damage to a woman's reproductive system, including infertility or possibly a fatal ectopic pregnancy. The study showed the test significantly shortened the time from a patient's arrival at the ED to test results, treatment, and discharge. It also reduced the time from specimen collection to STI result to 47 minutes per patient compared with an average of 25 hours for the standard-of-care lab-processed molecular tests. The Visby Medical test also resulted in significantly higher rates of appropriate treatment and lower rates of overtreatment with antibiotics for Chlamydia trachomatis and Neisseria gonorrhoeae infections compared with standard-of-care tests. MR. MATHERS is the associate editor of Emergency Medicine News.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".