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Record W2243567669 · doi:10.71781/12980

Causes multiples de décès chez les personnes âgées au Québec, 2000-2004

2010· dissertation· fr· W2243567669 on OpenAlexaboutno aff
Allison Blagrave

Bibliographic record

VenueOpen MIND · 2010
Typedissertation
Languagefr
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicineGynecologyArt

Abstract

fetched live from OpenAlex

Afin d'effectuer les classements et les analyses portant sur la mortalité selon la cause médicale de décès, il est d'usage d'utiliser uniquement la cause initiale de décès, qui représente la maladie ou le traumatisme ayant initié la séquence d'événements menant au décès. Cette méthode comporte plusieurs limites. L'analyse de causes multiples, qui a la qualité d'utiliser toutes les causes citées sur le certificat de décès, serait particulièrement indiquée pour mieux expliquer la mortalité puisque les décès sont souvent attribuables à plusieurs processus morbides concurrents. L'analyse des causes multiples de décès chez les personnes âgées au Québec pour les années 2000-2004 permet d'identifier plusieurs conditions ayant contribué au décès, mais n'ayant toutefois pas été sélectionnées comme cause ayant initié le processus morbide. C'est particulièrement le cas de l'hypertension, de l'athérosclérose, de la septicémie, de la grippe et pneumonie, du diabète sucré et de la néphrite, syndrome néphrotique et néphropathie. Cette recherche démontre donc l'importance de la prise en compte des causes multiples afin de dresser un portrait plus juste de la mortalité québécoise aux âges où se concentrent principalement les décès que le permet l'analyse de la cause initiale seule.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.041
GPT teacher head0.335
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

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
Published2010
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicInsurance, Mortality, Demography, Risk ManagementFrench-language works237,207