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Record W2021235085 · doi:10.4103/0019-5359.31159

Information and communication technology developments in asthma management: A systematic review

2007· review· en· W2021235085 on OpenAlexaff
Venkata R. Duvvuri

Bibliographic record

VenueIndian Journal of Medical Sciences · 2007
Typereview
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsYork University
Fundersnot available
KeywordsInformation and Communications TechnologyMedicineAsthmaICTSAsthma managementKey (lock)Knowledge managementProcess (computing)Information technologyComputer scienceWorld Wide WebImmunology

Abstract

fetched live from OpenAlex

This review aims to explain the progress of information and communication technology (ICT) applications in asthma management. Appropriate literature was printed out from the bibliographic databases and library source using relevant key phrases of ICT and asthma. The ICT developments from simple to complex modules to augment the conventional methods of asthma care with a caution of excessive reliance upon technology were discussed. However, it should be noted ICTs are for maximizing the human clinician's own ability to receive and process information as well as providing unique opportunities for patients, physicians, pharmacists and researchers.

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)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0080.012
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.114
GPT teacher head0.517
Teacher spread0.403 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations15
Published2007
Admission routes1
Has abstractyes

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