An umbrella review of resistance training to promote increases in muscle function and hypertrophy
Bibliographic record
Abstract
eview question / Objective The proposal is to undertake a structured, systematic umbrella review of systematic reviews and meta-analyses that have included randomized controlled trials (RCT) of resistance training (RT) and to identify Frequency, Intensity, Type and Time (FITT) principles that lead to the largest effects.PICO Questions for An umbrella review of resistance training to promote increases in muscle function and hypertrophy.Does chronic RT (I), compared to a comparator group (C), increase muscular strength, power, endurance, contraction velocity and hypertrophy (muscle biopsy, ultrasound, MRI, CT, DXA, BIA, creatinine, D3-Cr) (O) among younger (> 18yr) and older (> 55yr) adults (P)?The influence of resistance training (RT) program variables (Frequency [training session per week], Intensity [load, work to fatigue], Type [free weight, machine-guided], Time [under tension, high/low velocity]) in promoting gains in strength (variously measured), power, endurance, contraction velocity and hypertrophy in younger (18-55) adults.Rationale There are numerous systematic reviews of resistance training manipulating a multitude of training-related variables.The most effective prescription to promote gains in strength and hypertrophy is unknown.Condition being studied Resistance Training. METHODS Participant or population Adults >18 years. Intervention Resistance training.Comparator Control (no resistance exercise) OR an alternative prescription for resistance exercise.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".