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Record W6931572973 · doi:10.5683/sp3/dblsma

Search strategy for patient reported outcomes AND cancer immunotherapy

2022· dataset· en· W6931572973 on OpenAlexaff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsMcGill University
Fundersnot available
KeywordsCINAHLMEDLINEScopusGrey literatureCochrane LibraryCancer

Abstract

fetched live from OpenAlex

The notes field contains the full MEDLINE (Ovid) search strategy for patient reported outcomes AND cancer immunotherapy. The file in this dataset contains the full Ovid MEDLINE, Ovid Embase, Ovid PsycInfo, and Ebsco CINAHL search strategies for patient reported outcomes AND cancer immunotherapy. An iterative process was used for the data collection of this scoping review, with an initial screening done of the Ovid Medline and Ovid Embase search results (search date March 10 2021), and a second screening done of updated Ovid Medline and Ovid Embase search results as well as searches translated into Ovid PsycInfo, and Ebsco CINAHL (search date December 17, 2021). Also included in the file are details pertaining to the grey literature search including search strategies used in Scopus and Proquest Global Theses and Dissertations.

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)0.010
metaresearch head score (Gemma)0.069
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.296
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.069
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0270.039
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.2960.040

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 head0.144
GPT teacher head0.453
Teacher spread0.309 · 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
GenreDataset

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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