Recherche des références bibliographiques à intégrer dans la matrice de recherche de l’étude SPICES dans les recommandations internationales sélectionnées : <i>Arrêt de la consommation de tabac : du dépistage individuel au maintien de l’abstinence en premier recours. Haute Autorité de Santé. 2014 Oct</i> et <i>Recommendations for prevention of weight gain and use of behavioral and pharmacologic interventions to manage overweight and obesity in adults in primary care. Canadian Medical Association Journal. 2015 Feb 17</i>. Détermination des interventions pour la prévention primaire des maladies cardiovasculaires efficaces et faisables en soins primaires et/ou dans la communauté, à partir des références (partie 1) extraites des recommandations de bonnes pratiques analysées dans le cadre de l’étude SPICES
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the first cause of death worldwide. The control of cardiovascular risk factors is complex and expensive. The SPICES project will implement primary prevention interventions for CVD in the community. A selection of recommendations (RBP) according to the ADAPTE method was carried out before this work. Method : recommendations from the RBP "Stop smoking cessation" and "CMAJ Recommendations for prevention of weight gain" were included if they had a high level of evidence and included non-pharmacological interventions to prevent CVD in primary care. References that supported them were incorporated into an 8-part research matrix. Part 1 has been analyzed in this work. References were selected if they concerned effective intervention studies that were applicable in primary care. Results : 24 HAS references and 44 CMAJ references were included. The inclusion of a CMAJ recommendation was decided by consensus despite its low level of evidence. From Part 1 of the matrix, 95 references were excluded out of 100. 5 were metanalyses and literature reviews analyzed separately. Discussion : the analysis of the RBPs showed the absence of practical details for the implementation of the recommendations. The extraction of references supporting these recommendations was necessary. Some recommendations relevant to the project were excluded because of their level of evidence. An exception was made for CMAJ. 63 references out of 100 in Part 1 were excluded because of their context. Recommendations from RBPs are too general to be implemented and references that support them, usually too controlled to be applicable in the community.
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 | 0.037 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".