High Outcome-Reporting Bias in Randomized-Controlled Trials of Acupuncture for Cancer Chemotherapy-Induced Nausea and Vomiting: A Systematic Review and Meta-Epidemiological Study
Bibliographic record
Abstract
Selective outcome-reporting bias refers to the selective reporting of a subset of study findings. This methodological limitation may occur in cancer-related acupuncture studies, where valid empirical studies on psychometric performance are still lacking. We assessed the risk of selective outcome reporting bias in studies published in English that were included in a systematic review on acupuncture for preventing cancer chemotherapy-induced nausea and vomiting. For each study, we searched for registry availability and, if present, assessed its validity. We described each study outcome (nausea, vomiting, or both) according to the following seven items: type of outcome, domain, specific measurement, specific metric, type of data, methods of aggregation, and timepoint unit and time. Eleven studies published between 1987 and 2019 in English were evaluated. Only four (36%) had a registry, of which only two were prospective and therefore considered valid. Discrepancies were found in the specific measurement of the outcome in two studies and in the specific metric. In many other cases, discrepancies were not evaluable due to missing information. No study reported complete outcomes as planned in the published protocol. Communication about the importance of prospective trial registration, including outcome details, should be enforced to reduce the risk of selective outcome reporting bias in oncology acupuncture studies.
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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 | MetaresearchMeta-epidemiology (broad)Meta-epidemiology (narrow) Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) 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 | 0.273 | 0.468 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".