MétaCan
Menu
Back to cohort
Record W7028939837

Goede zorg, een kwestie van ervaring

2007· article· en· W7028939837 on OpenAlexaboutno aff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careQuality (philosophy)Quality managementHealthcare systemCase fatality rateHealth professionals
DOInot available

Abstract

fetched live from OpenAlex

In an evidence-based review of the relationship between volume and quality of care, the independent Dutch Institute for Healthcare Improvement (CBO) concluded that volume appears to be related to outcome for certain surgical procedures (case fatality after pancreatic and oesophageal cancer) and that quality of care might be improved by centralisation. The Dutch Institute for Healthcare Improvement also identified conditions required for centralisation, particularly acceptance by professionals and hospitals. In the USA, programmes to improve quality ofcare initiated by the Leapfrog Group using volume criteria or, more recently, using 'public reporting' and 'pay for performance' principles have led to improvements in quality. In Canada, the Surgical Oncology programme within the Cancer System Quality Index programme has reduced case fatality following pancreatic resection. The Canadian programme was based not only on volume but also on standards, guidelines, rapid access strategies and publicly available performance assessments. In The Netherlands, the Dutch Health Care Inspectorate is introducing the so-called performance indicators of care. Other initiatives are underway to develop a system with multiple quality criteria as in Canada. These programmes should not be restricted to surgical procedures but should include complex procedures in other specialties as well

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.103
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1030.013

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 head0.037
GPT teacher head0.311
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueData Archiving and Networked Services (DANS)Same topicGender, Security, and ConflictFrench-language works237,207