A methodological review of the quality of reporting of surveys in transfusion medicine
Bibliographic record
Abstract
BACKGROUND: Surveys are a common tool used to gather information about practices across many medical specialties. The quality of survey reporting impacts the strength of any conclusions. Thorough and accurate reporting of survey-based research is critical for evaluation of the validity, reliability, and generalizability of the results. The objective of this study was to appraise the quality of recently reported surveys in transfusion medicine (TM). STUDY DESIGN AND METHODS: A systematic review of the literature was performed to identify studies evaluating clinical practices in TM that used a questionnaire as the research tool and were published between January 2001 and November 2017. Manuscripts that met eligible criteria were appraised using a modified Survey Reporting Guideline questionnaire. RESULTS: The search identified 1632 references, from which 54 abstracts met eligibility criteria for analysis. Only seven (13%) manuscripts reported reliability and validity of the survey tool, 26 (48%) provided a description of the survey population and sample frame, and 11 (20%) indicated the representativeness of the underlying population. Additional reporting limitations included 31 (57%) manuscripts reporting the response rate calculation, two (4%) the analysis of nonresponse error, nine (17%) the method description for handling of missing data, 11 (20%) the analysis of responder and nonresponder characteristics, and 23 (43%) explicitly discussed the generalizability of the results. CONCLUSION: Our findings document quality deficiencies in the reporting of research conducted using surveys in TM. Validated guidelines for the reporting of survey-based clinical research should be developed and applied to improve the quality of survey reporting in TM.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.522 | 0.794 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.033 | 0.035 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".