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Terapia tópica para el tratamiento del dolor en heridas neoplásicas malignas: protocolo de revisión de alcance

2021· review· es· W3199189786 on OpenAlexaff
Suzana Aparecida da Costa Ferreira, Carol Viviana Serna González, Adriane Aparecida da Costa Faresin, Magali Thum, Talita dos Santos Rosa, Kevin Woo, Vera Lúcia Conceição de Gouveia Santos

Bibliographic record

VenueJournal of Wound Care · 2021
Typereview
Languagees
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineSystematic reviewMEDLINECINAHLHumanitiesGeneral surgerySurgeryPsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: A total of 14.5% of cancer patients develop malignant neoplastic wounds (MNW), characterised as friable, exudative, fetid, bleeding, and painful. Some studies report that all patients with MNW experience pain, but there is lack of scientific evidence to support their treatment. OBJECTIVE: To map and examine the existing evidence on topical therapies to manage pain in adult patients with MNW. METHOD: A scoping review protocol was designed, according to the Joanna Briggs Institute (JBI) methodology. The databases CINAHL, LILACS, Embase, Scopus, Web of Science, PubMed, Cochrane, NICE, Scopus, JBISRIR and the grey literature, for searching published and unpublished studies in English, Portuguese and Spanish. The selection will be made by at least two reviewers. The summary of the results will be narrative, with graphs and tables. Qualitative and quantitative studies and reviews will be included, describing the use of topical pain therapies in patients with MNW. CONCLUSION: This study will allow to classify and discuss the available topical therapies, and to recommend future primary studies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.378
Teacher spread0.347 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2021
Admission routes1
Has abstractyes

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