NHS England postpones roll-out of care.data programme by six months
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
<h3>Abstract</h3> <h3>Objective</h3> To guide family physicians in creating preventive screening and treatment plans for their elderly patients. <h3>Sources of information</h3> The MEDLINE database was searched for Canadian guidelines on primary health care and the elderly; guidelines or meta-analyses or practice guidelines or systematic reviews related to mass screening in those aged 80 and older and the frail elderly, limited to between 2006 and July 2016; and articles on preventive health services for the elderly related to family practice or family physicians, limited to English-language publications between 2012 and July 2016. <h3>Main message</h3> Estimating life expectancy is not an easy or precise science, but frailty is an emerging concept that can help with this. The Canadian Task Force on Preventive Health Care offers cancer screening guidelines, but they are less clear for patients older than 74 years and management plans need to be individualized. Estimating remaining years of life helps guide your recommendations for preventive screening and treatment plans. Risks often increase along with an increase in frailty and comorbidity. Conversely, benefits often diminish as life expectancy decreases. Preventive management plans should take into account the patient’s perspective and be mutually agreed upon. A mnemonic device for key primary care preventive areas—<i>CCFP</i>, short for cancer, cardiovascular disease, falls and osteoporosis, and preventive immunizations—might be useful. <h3>Conclusion</h3> Family physicians might find addressing the following areas helpful when considering a preventive health intervention: age, life expectancy (including concept of frailty), comorbidities and functional status, risks and benefits of screening or treatment, and values and preferences of the patient.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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