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Record W6981649798

Étude observationnelle des prescriptions des IPP chez les personnes de 65 ans et plus en officine

2022· dissertation· en· W6981649798 on OpenAlexaboutno aff

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2022
Typedissertation
Languageen
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionAdverse effectGERDIndigestionRisk assessmentDisease
DOInot available

Abstract

fetched live from OpenAlex

PPIs are indispensable medicines in many indications. The absence of immediate adverse effects has, however, led to significant exposure in the general population. In many cases, these treatments are used over very long periods of time. For several years, some studies have shown the increased risk of bone fractures, digestive infections, or kidney failure... In 2019, the HAS issued its opinion on the use of PPIs in France and some guidelines on the main observed misuses. Efforts to prescribe and de-prescribe these drugs must be made in a multi-professional manner with the aim of maintaining an optimal benefit/risk ratio for the patient. The study examined the pattern of prescribing and use of PPIs in people 65 years of age and older in informal settings. The majority of patients use PPIs over the long term, for non-MA or unwarranted indications. The hygieno-dietetic rules are an asset in the management of digestive pathologies, including dyspepsia and GERD allowing, in many cases, not to use a treatment or to allow the cessation of a PPI. Some countries, such as Canada, are more advanced in re-evaluating existing treatments, including PPIs. Deprescription algorithms are proposed and many tools are made available to re-evaluate the treatment by IPP in patients using them long term. PPIs are treatments that are more than proven to be effective. Like all drugs, they expose the patient to a potential risk if their use is not reasoned and justified. It is therefore necessary that the treatment be re-evaluated over time and deleted if necessary. Indeed, the benefit/risk ratio must be a primary objective, without losing the chance of improving the patient’s health.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.051
GPT teacher head0.348
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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