Methods for qualitative and quantitative analysis of pain and quality of life validated in Brazil: systematic review
Bibliographic record
Abstract
ABSTRACT Objective: The need to quantify pain and quality of life (QoL) is essential for professionals considering their therapeutic approach. The goal of this review is to identify the methods to perform qualitative and quantitative analysis of pain and QoL validated in Brazil. Methods: Review by the LILACS, SciELO, MedLine and Google Scholar databases with the descriptors: methods, qualitative analysis, quantitative analysis, pain and quality of life. Inclusion criteria: articles published in Portuguese and in English in the period from 1996 to 2015. Exclusion criteria: incomplete texts, articles that did not address the subject of study and duplicate articles in the databases. Results: After applying the eligibility criteria, 27 articles were selected for reading, being that one article was excluded by presenting irrelevant result and another was excluded by duplication. From the 25 articles, one was published in 2015, three in 2014, one in 2013, three in 2012, five in 2011, two in 2010, three in 2009, four in 2008, two in 2004 and one in 1996. In relation to the studies, nine were clinical trials, 10 systematic reviews, five cross-sectional studies and one essay. Conclusion The most frequently methods applied are the VAS and the McGill’s Questionnaire, considering the multidimensional pain assessment. The most commonly used questionnaire to evaluate QoL is the SF-36. There is great difficulty to classify methods for assessing pain and QoL (qualitative or quantitative methods), since many authors report the same method when addressing the two interfaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".