A Systematic Review of Qualitative Research in Hand Surgery
Bibliographic record
Abstract
No formal review of qualitative research in hand surgery has been previously performed. The primary objective of this study was to evaluate the reporting quality of hand surgery qualitative research with the Standards for Reporting Qualitative Research (SRQR), a 21-item checklist. The secondary objectives were to describe qualitative research in hand surgery by domain, determine differences in reporting quality based on use of a reporting guideline, publication of SRQR and journal of publication, and to identify important outcomes in hand surgery conditions. Fifty-five studies were included from MEDLINE, Embase, PsycINFO, and Emcare. The median SRQR score was 16. The lowest reported sections were context, data collection methods , and data analysis. Qualitative research was found in multiple domains of hand surgery. There was a significant difference between papers that used a reporting guideline and studies published after the publication of the SRQR. Clinical/medical/basic science journals had the highest median SRQR score. Outcomes identified were pain for carpal tunnel syndrome and pain, function, unintentional harm, recurrence, and recovery time for Dupuytren disease. To further improve reporting quality in hand surgery qualitative research, we recommend that investigators ensure they provide rationale for their methodology and become familiar with the SRQR guidelines.
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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 | 0.171 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".