Personnes âgées : transmission de l'information médicale en gériatrie
Bibliographic record
Abstract
Ageing of French population is a phenomenon know for several years with currently more than a quarter of the population being over 60 years old. This ageing is not homogeneous between individuals: for same it is successful, for others it is usual or pathological. Thus, more than a million people have cognitive function disorders which are more or less disabling. At the same time, French society is evolving with a growing desire for autonomy in many areas such as health with a desire to definitively get out of paternalistic medicine practiced before. How to reconcile cognitive impairment and the provision of medical information to obtain free and informed consent and allow elderly people to be actors in their health? To lead this reflection, I conducted, between January and March 2019, an unicentric practice survey with distribution of questionnaires composed of 4 open questions to geriatricians of the geriatric shortstay service of CHU in Caen. The results show that these specialits promote the autonomy of their patients according to different methods: repetition of information given with language adapted to the school level, reformulation by the patient, systematic geriatric evaluation, solicitation of relatives (family, trusted person, caregiver). In addition, other means are being expanded and already used by paramedical staff such as temporality and valorization of body language with the use of the senses.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".