Spin of information and inconsistency between abstract and full text in RCTs investigating upper limb rehabilitation after stroke: An overview study
Bibliographic record
Abstract
BACKGROUND: Researchers may be tempted to favorably distort the interpretation of their findings when reporting the abstract (i.e., spin). Spin bias overemphasizes the beneficial effects of the intervention compared with the results shown in the full text. OBJECTIVE: To assess the occurrence of spin bias and incompleteness in reporting abstracts in post-stroke upper limb (UL) rehabilitation randomized clinical trials (RCTs). METHODS: A sample of 120 post-stroke UL rehabilitation RCTs (indexed in PEDro database), published in English between 2012 and 2020, was included. The completeness of reporting and spin were assessed using the Consolidated Standards of Reporting Trials for Abstracts (CONSORT-A) and the spin checklist. The relationship between CONSORT-A and spin checklist scores with RCT and journal characteristics was assessed. RESULTS: CONSORT-A and spin checklist scored 5.3±2.4 (max 15-points, higher scores indicating better reporting) and 5.5±2.0 (max 7-points, higher scores indicating presence of spin), respectively; Significant differences were detected between abstract and full-text scores in the CONSORT-A checklist (p < 0.01) and the spin checklist (p < 0.01). Items of the CONSORT-A checklist in the abstracts and full text showed a fair agreement (k = 0.31), while a moderate agreement (k = 0.59) for the spin checklist was detected. Completeness of abstract was associated (R2 = 0.46) with journal Impact Factor (p < 0.01), CONSORT Guideline endorsement (p = 0.04), and abstract word number (p = 0.02). A lower spin was associated with a higher journal Impact Factor (p = 0.01) and CONSORT Guideline endorsement (p = 0.01). CONCLUSIONS: Post-stroke UL rehabilitation RCTs abstracts were largely incomplete showing spin. Authors, reviewers, publishers, and stakeholders should be aware of this phenomenon. Publishers should consider allowing more words in abstracts to improve the completeness of reporting abstracts. Although we have investigated only stroke rehabilitation, our results suggest that health care professionals of all disciplines should avoid clinical decision-making based solely upon abstracts.
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 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 | 0.383 | 0.636 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".