Response
Bibliographic record
Abstract
Dear Editor-in-Chief, We appreciate the letter to the editor (1) regarding our recent paper published in this journal, as well as the opportunity to respond to these comments. The aim of our paper was to investigate whether supervised learning techniques could be used to predict the occurrence of hamstring strain injury (HSI), using risk factor data measured across multiple seasons. Eccentric knee flexor force (N) was measured during the Nordic hamstring exercise (NHE). The authors (1) suggest that the muscle forces that occur during the NHE do not reflect the high muscle force observed during high-speed running. There is currently no evidence to either support or oppose this argument. However, there is evidence to suggest that NHE-derived eccentric knee flexor force is associated with HSI risk in elite Australian footballers (2) and other cohorts (3). In their letter (1), the authors propose that eccentric exercises at the knee should be performed with the hip flexed in order to expose the hamstrings to longer lengths and they cite a paper supporting this argument (4). The paper cited also postulates that in order to stimulate fascicle length adaptations, exercises must be performed under load through full muscle excursion range (4). However, there are data to suggest that the NHE results in greater biceps femoris long head activation and fascicle length adaptations than 45° hip extension (which involves a flexed hip and extended knee), despite the NHE having smaller muscle excursions (3). The authors (1) also claim that the NHE will not lead to “heavy muscle loads” and they cite one study in which 5 weeks of NHE training did not increase eccentric knee flexor strength, although it did stimulate hamstring muscle hypertrophy. We would like to direct their attention to a significant number of studies that have reported increases in eccentric knee flexor strength after NHE training, measured via isokinetic dynamometry (5,6) and the NHE (7,8). In addition, NHE training interventions have been widely implemented in both research and sport and have been shown to reduce rates of HSI (3). Given the prevalence of the NHE in research and sport alike, we believe that investigating the predictive ability of NHE-derived eccentric knee flexor force is warranted. We would also like to note that the ability to capture large data sets is an ongoing limitation of sports injury research. In our paper, 362 measures of eccentric knee flexor force were conducted at the start of preseason across two seasons. Despite these numbers, we concluded that larger data sets may be needed to investigate the predictive ability of injury risk factors. The suitability of alternative methods for large-scale, prospective data collection, such as isokinetic and handheld dynamometry, has been questioned previously (9). Isokinetic dynamometry can be time consuming and costly, whereas handheld dynamometry requires high levels of both operator skill and strength to collect valid and reliable data (9). Joshua D. Ruddy Nirav Maniar School of Exercise Science Australian Catholic University Melbourne, AUSTRALIA Morgan D. Williams School of Health Sport and Professional Practice Faculty of Life Sciences and Education University of South Wales UNITED KINGDOM Steven Duhig School of Allied Health Sciences Griffith University Gold Coast, AUSTRALIA Ryan G. Timmins Jack Hickey School of Exercise Science Australian Catholic University Melbourne, AUSTRALIA Matthew N. Bourne School of Allied Health Sciences Griffith University Gold Coast, AUSTRALIA David A. Opar School of Exercise Science Australian Catholic University Melbourne, AUSTRALIA
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 | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.053 | 0.040 |
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".